<｜begin▁of▁sentence｜>### Instruction:
Imagine we're looking at the social dynamics of a group of youths in a community center, akin to observing the interactions within a karate club. In this microcosm, each individual can be seen as a node within a network of relationships, similar to the nodes in a karate club graph. Just as we assess the youths based on their levels of involvement and influence within the group, we can analyze the nodes in this network based on their core numbers, which indicate the depth of their connections within the social structure.

Now, you're tasked with focusing on a specific subset of this network. Your goal is to identify and closely monitor a group of individuals who are not just casually associated with the others but are significantly integrated into the group's fabric. Specifically, you're looking for those who are at a particular level of involvement - let's say they have direct ties to at least three other individuals within this community. In network analysis terms, you're interested in the subgraph formed by the nodes with a core number of exactly 3, or the 3-shell of the graph, to better understand this group's structure and dynamics.

For clarity, knowing about a specific GML file would be like having a detailed report with data on all the interactions within the community center, enabling us to drill down into the specifics. However, since we're not referring to a particular file right now, our focus remains conceptual. Your assignment is akin to extracting a list of those specific youths from the broader report, based on their established level of interaction, for more targeted support and intervention.
### Response:
import networkx as nx

# Create a graph
G = nx.karate_club_graph()

# Compute the k-shell of the graph
k_shell_subgraph = nx.k_shell(G, k=3)

# Draw the k-shell subgraph
import matplotlib.pyplot as plt
nx.draw(k_shell_subgraph, with_labels=True)
plt.show()
<|EOT|>
