human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modularity belief propagation on multilayer networks to detect significant community structure"):
  Abstract: Modularity based community detection encompasses a number of widely used, efficient heuristics for identification of structure in single- and multilayer networks. Recently, a belief propagation approach to modularity optimization provided a useful guide for identifying non-trivial structure in a way that other optimization heuristics have not. In this paper, we extend modularity belief propagation to multilayer networks. As part of this development, we also directly incorporate a resolution parameter. We show that the resolution parameter affects the convergence properties of the algorithm and yields different community structures than the baseline. We demonstrate our extension on synthetic multilayer networks, showing how our tool achieves near optimal performance and prevents overfitting. We highlight these advantages in comparison to another widely used tool, GenLouvain for multilayer modularity. Finally, we apply multilayer modularity belief propagation to two real-world multilayer networks and discuss practical concerns in implementing our method, which we have released as a Python package for general use.

Includes members:
  Member "relating modularity maximization and stochastic block models in multilayer networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models")
  Member "post processing partitions to identify domains of modularity optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supracentrality analysis of temporal networks with directed interlayer coupling")
  Member "the many facets of community detection in complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "embedding based silhouette community detection")
  Member "the ground truth about metadata and community detection in networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameterized objectives and algorithms for clustering bipartite graphs and hypergraphs")
  Member "graph spectra and the detectability of community structure in networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fragility of spectral clustering for networks with an overlapping structure")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.SI(Social and Information Networks)