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 "quantitative function and algorithm for community detection in bipartite networks"):
  Abstract: Community detection in complex networks is a topic of high interest in many fields. Bipartite networks are a special type of complex networks in which nodes are decomposed into two disjoint sets, and only nodes between the two sets can be connected. Bipartite networks represent diverse interaction patterns in many real-world systems, such as predator-prey networks, plant-pollinator networks, and drug-target networks. While community detection in unipartite networks has been extensively studied in the past decade, identification of modules or communities in bipartite networks is still in its early stage. Several quantitative functions proposed for evaluating the quality of bipartite network divisions are based on null models and have distinct resolution limits. In this paper, we propose a new quantitative function for community detection in bipartite networks, and demonstrate that this quantitative function is superior to the widely used Barber's bipartite modularity and other functions. Based on the new quantitative function, the bipartite network community detection problem is formulated into an integer programming model. Bipartite networks can be partitioned into reasonable overlapping communities by maximizing the quantitative function. We further develop a heuristic and adapted label propagation algorithm (BiLPA) to optimize the quantitative function in large-scale bipartite networks. BiLPA does not require any prior knowledge about the number of communities in the networks. We apply BiLPA to both artificial networks and real-world networks and demonstrate that this method can successfully identify the community structures of bipartite networks.

Includes members:
  Member "local community extraction in directed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey of community search over big graphs")
  Member "community detection in bipartite networks using weighted symmetric binary matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization")
  Member "maximizing barber s bipartite modularity is also hard":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "measuring and modeling bipartite graphs with community structure")
  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 "overlapping community detection in complex networks using symmetric binary matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition")
  Member "discovering link communities in complex networks by exploiting link dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "permanence and community structure in complex networks")
  Member "scalable spectral algorithms for community detection in directed networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "detecting highly cyclic structure with complex eigenpairs")

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)