<｜begin▁of▁sentence｜>### Instruction:
Imagine you're curating an art exhibit that links different styles (like A and B) with various themes (like 1, 2, and 3). You've decided to create a visual map, connecting styles and themes with lines to show their relationships—similar to a collaborative project where artists ('A' and 'B') and themes (1, 2, and 3) come together. As the curator, you're particularly interested in understanding which artists or themes play a pivotal role in connecting the others. To give your exhibit depth, you want to calculate the influence each artist or theme has within this web of connections, identifying pivotal points that connect various parts of the exhibit.

Can you translate this curiosity into a numerical form, computing the betweenness centrality for each artist and theme within our exhibit's network? Remember, your connection blueprint consists of the following pairs: artist 'A' connects with themes 1 and 2, 'B' with themes 1 and 2 as well, 'C' is solely connected with theme 3, and 'D' also joins with theme 3. Consider artists 'A' and 'B' as one group of your bipartite mapping, akin to a specific style or category of art in your exhibit.
### Response:
import networkx as nx
from networkx.algorithms import bipartite

# Create a bipartite graph
B = nx.Graph()
B.add_edges_from([('A', 1), ('B', 1), ('A', 2), ('B', 2), ('C', 3), ('D', 3)])

# Define the top nodes (one bipartite node set)
top_nodes = {'A', 'B'}

# Calculate bipartite betweenness centrality
betweenness = bipartite.betweenness_centrality(B, nodes=top_nodes)
print(betweenness)
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