<｜begin▁of▁sentence｜>### Instruction:
Imagine we are developing a virtual reality simulation of a small city, where the connections between various points of interest are represented as pathways with varying levels of difficulty to traverse. Our nodes 'A', 'B', 'C', and 'D' represent these points of interest, and we need to determine how centrally located each one is within our network for an efficient navigation system. The pathways connecting our points of interest have different traversal costs associated with them: the path from 'A' to 'B' costs 2 units of energy, 'B' to 'C' costs 3 units, 'C' to 'A' costs 1 unit, and 'C' to 'D' costs 4 units.

For the AI characters navigating this virtual space, we want to assess their efficiency moving within the city by calculating the current-flow closeness centrality of each point of interest, using the 'lu' solver to handle the underlying linear systems. How would this computation affect the centrality values for each point or node, and thereby influence the AI's navigation decisions? Integrating these centrality calculations could improve the fluidity and realism of the simulation.
### Response:
import networkx as nx

# Create a graph
G = nx.Graph()

# Add weighted edges
G.add_edge('A', 'B', weight=2)
G.add_edge('B', 'C', weight=3)
G.add_edge('C', 'A', weight=1)
G.add_edge('C', 'D', weight=4)

# Compute the current-flow closeness centrality
centrality = nx.current_flow_closeness_centrality(G, normalized=True, weight='weight', solver='lu')

# Display the centrality of nodes
for node, c in centrality.items():
    print(f'Node {node}: {c:.2f}')
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