<｜begin▁of▁sentence｜>### Instruction:
Imagine you're co-authoring a novel where the characters navigate a labyrinth of social connections, intricately weaving through a series of encounters. Within this narrative, you've designed a unique network of interactions—a 'bipartite graph'—where characters from the esteemed list ['A', 'B', 'C', 'D', 'E'] have exclusive meetings with individuals belonging to a secondary, unnamed group, as indicated by their encounters: [('A', 1), ('B', 1), ('A', 2), ('C', 2), ('D', 3), ('E', 3)]. 

As the architect of this web of characters, you’ve been asked to subtly illustrate the influence that these protagonists exert within the social sphere they inhabit. To do this, can you conceptualize the 'degree centrality' for each character—'A', 'B', 'C', 'D', 'E'—providing a metric of their prominence based on the number of direct connections each has within this shared world? This measure will not only empower your readers to grasp the depth of the characters' networking prowess but also allow for an underlying commentary on the social dynamics at play.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
# Add edges between node sets U and V
B.add_edges_from([('A', 1), ('B', 1), ('A', 2), ('C', 2), ('D', 3), ('E', 3)])

# Define one set of bipartite nodes
U = set(['A', 'B', 'C', 'D', 'E'])
V = set([1, 2, 3])

# Ensure the bipartite graph is valid
assert bipartite.is_bipartite_node_set(B, U)
assert bipartite.is_bipartite_node_set(B, V)

# Compute the degree centrality for nodes in set U
centrality = bipartite.degree_centrality(B, U)
print(centrality)
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