<｜begin▁of▁sentence｜>### Instruction:
Imagine you're preparing for a group of inquisitive students coming to the museum to learn about connections between different eras and cultures, represented as an exhibit network. You have a map of the exhibit, where the halls are marked as points 'A' through 'F', and the pathways between these are your connections: [('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), ('F', 'C')].

Now, you want to organize the information in a way that helps you identify the best route for the tour, ensuring each exhibit is visited without unnecessary detours, in essence, creating a structured flow for your tour.

Could you craft a junction tree from this map of the exhibit with NetworkX to assist you in streamlining this educational experience? This representation would ideally reduce redundancies in the tour path, ensuring a cohesive narrative for the students' journey through the epochs and links between the cultures on display.
### Response:
import networkx as nx
from networkx.algorithms.tree.decomposition import junction_tree

# Create a graph
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'C'), ('C', 'D'), ('D', 'E'), ('E', 'F'), ('F', 'C')])

# Compute the junction tree of the graph
j_tree = junction_tree(G)
print('Junction Tree Nodes:', j_tree.nodes())
print('Junction Tree Edges:', j_tree.edges())
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