<｜begin▁of▁sentence｜>### Instruction:
Imagine you're working as a sports agent, and you have a group of five athletes you're managing: Athlete 0, Athlete 1, Athlete 2, Athlete 3, and Athlete 4. You're looking to design a schedule for team events and interviews that effectively showcases each athlete at least once. 

Now, think of the "team events and interviews" as 'edges', and each of your athletes as 'nodes'. Currently, the schedule (or 'edge set') you're considering looks like this: events featuring both Athlete 0 and Athlete 1, another with Athlete 0 and Athlete 2, then Athlete 0 and Athlete 3, one with both Athlete 1 and Athlete 2, and finally an event with Athlete 1 and Athlete 3.

Your task, as their agent, is to find the least number of these joint events that still ensures each athlete participates at least once. In essence, you're finding the minimum edge coverage for your network of athletes. How can you arrange their appearances to achieve this goal while optimizing their time? Remember, the goal is to cast the spotlight on all of them without over-scheduling.

(Note: There is no GML file or external data to be included.)
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])

# Find the minimum edge cover
min_cover = nx.min_edge_cover(G)

# Print the edges in the minimum edge cover
print(sorted(min_cover))
<|EOT|>
