<｜begin▁of▁sentence｜>### Instruction:
Imagine you're organizing a special dinner event for a small, exclusive group of guests, where the main attraction, apart from your culinary masterpieces, is a themed discussion on the intricate connections within a social network—akin to a Growing Network with Copying graph. To make this event both engaging and visually stimulating, you wish to present a graphic representation of such a network, with a total of five "guests" or nodes, if you will.

To ensure that the discussion can be replicated or revisited in future events, you'd like to prepare by using a specific "recipe" that can be followed to create the same network each time. Think of it like using a seed value, much like the way you'd use a specific herb to ensure the flavor profile of your dish remains consistent; for this network, the seed value you'll use is 42.

Now, in order to bring this concept to life, you're considering employing the 'gnc_graph' function available in NetworkX, a tool that's as essential to data scientists as a good knife is to a chef. How would you go about using this 'gnc_graph' API to create the visual representation of your themed Growing Network with Copying graph? Remember, the seed must be set to 42 to ensure the network's "flavor" is reproducible at your next avant-garde dinner gathering.
### Response:
import networkx as nx

# Set the number of nodes
n = 5

# Set the seed for reproducibility
seed = 42

# Generate a GNC graph with n nodes
G = nx.gnc_graph(n, seed=seed)

# You may use nx.DiGraph() for creating using parameter if needed
# For example: G = nx.gnc_graph(n, create_using=nx.DiGraph(), seed=seed)

# Now we can print the nodes and edges
print('Nodes:', G.nodes())
print('Edges:', G.edges())
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