<｜begin▁of▁sentence｜>### Instruction:
Imagine you're tending to a diverse ecosystem, much like mapping the intricate interactions within a wildlife sanctuary. You have a network that represents the interconnected habitat, where each node could signify a different species or environmental factor. In your network, the proximity of one species to another in terms of their ecological impact is paramount, akin to the closeness centrality in a graph.

In order to visualize this ecological network effectively, with an emphasis on highlighting the varying degrees of centrality each species has within the habitat, you'd want to employ a spectrum of colors. Each color would correlate to the closeness centrality of the nodes, allowing you to swiftly identify which species are keystones in maintaining the balance of the sanctuary.

How might you artistically apply this spectrum of hues, such that it accurately reflects the closeness centrality values of these nodes in your graph? Take into account that you'd need to utilize something akin to the color mapping tools found in the NetworkX library, blending the realm of data visualization with your hands-on caretaking of the wildlife network.
### Response:
import networkx as nx
from graspologic.layouts import sequential_colors

# Generate a synthetic graph with node values
G = nx.karate_club_graph()
values = {node: val for node, val in enumerate(nx.closeness_centrality(G).values())}

# Use sequential_colors to map node values to colors
node_to_color = sequential_colors(values, light_background=True)

# Print the color mappingor node, color in node_to_color.items():
    print(f'Node: {node}, Color: {color}')
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