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 "on equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization"):
  Abstract: Community structures detection in complex network is important for understanding not only the topological structures of the network, but also the functions of it. Stochastic block model and nonnegative matrix factorization are two widely used methods for community detection, which are proposed from different perspectives. In this paper, the relations between them are studied. The logarithm of likelihood function for stochastic block model can be reformulated under the framework of nonnegative matrix factorization. Besides the model equivalence, the algorithms employed by the two methods are different. Preliminary numerical experiments are carried out to compare the behaviors of the algorithms.

Includes members:
  Member "equivalence between modularity optimization and maximum likelihood methods for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mga momentum gradient attack on network")
  Member "community detection in bipartite networks using weighted symmetric binary matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "community detection in complex networks using density based clustering algorithm and manifold learning")
  Member "overlapping community detection in networks with positive and negative links":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detection and localization of change points in temporal networks with the aid of stochastic block models")
  Member "efficiently inferring community structure in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "extracting hierarchical backbones from bipartite networks")
  Member "spectral redemption in clustering sparse networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "overlapping community detection in complex networks using symmetric binary matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition")

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)