<｜begin▁of▁sentence｜>### Instruction:
As an animator creating visual content for various media, you've been tasked with enhancing the realism and depth of a network visualization for a documentary on complex systems. You have access to a network dataset stored in a GML file named "cond-mat-2005.gml," representing interactions in a scientific community. To better understand the community structure within this network, you're exploring community detection techniques.

One approach involves using the 'sbm_dl' function from the 'cdlib' library, which specializes in community detection. This function employs a stochastic block model to identify communities within the network by minimizing its description length, aided by an agglomerative heuristic.

Your task is to utilize this function to analyze the 'cond-mat-2005' graph and extract its underlying community structure. Once identified, these communities will provide valuable insights into how scientists within the field collaborate and interact, aiding in the visualization process for the documentary.
### Response:
import networkx as nx
from cdlib import algorithms

# Load the condensed matter collaborations 2005 graph
G = nx.read_gml('cond-mat-2005.gml')

# Apply the SBM_DL algorithm to detect communities
coms = algorithms.sbm_dl(G)

# Print the communities
for community in coms.communities:
    print(community)
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