<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working on a project where you're analyzing the structural integrity and performance of a composite material under various stress scenarios. The composite is composed of different materials represented by nodes, and the interactions between these materials are symbolized by edges, creating a complex network of relations. In this network model, the edge set is represented by interactions [(1, 2), (2, 3), (3, 4), (1, 3), (1, 4)].

For the purpose of optimizing the composite's performance, you've identified two critical materials within this network, corresponding to nodes 2 and 3. You're interested in determining the role these materials play in the overall connectivity and stress distribution of the composite.

To quantify this, you aim to measure the group betweenness centrality of nodes 2 and 3 using NetworkX. This will help you understand how often these particular materials act as bridges along the shortest path between other materials in the network, potentially identifying areas for material reinforcement or indicating where to focus R&D efforts for improving the composite.

Could you proceed with this analysis and compute the group betweenness centrality for the group of materials represented by nodes [2, 3] in the structure's network using NetworkX?
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(1, 2), (2, 3), (3, 4), (1, 3), (1, 4)])

# Define the group of nodes for which we want to find group betweenness centrality
C = [2, 3]

# Calculate group betweenness centrality
betweenness = nx.group_betweenness_centrality(G, C, normalized=True, weight=None, endpoints=False)

print("Group Betweenness Centrality for group {} is: {}".format(C, betweenness))
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