human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "maximizing barber s bipartite modularity is also hard"
  Abstract: Modularity introduced by Newman and Girvan (Phys Rev E 69:026113, 2004) is a quality function for community detection in networks. Numerous methods for modularity maximization have been developed so far. In 2007, Barber (Phys Rev E 76:066102, 2007) introduced a variant of modularity called bipartite modularity which is appropriate for bipartite networks. Although maximizing the standard modularity is known to be NP-hard, the computational complexity of maximizing bipartite modularity has yet to be revealed. In this study, we prove that maximizing bipartite modularity is also NP-hard. More specifically, we show the NP-completeness of its decision version by constructing a reduction from a classical partitioning problem.

Connected through hyperedge HE-1 (induced by source paper "measuring and modeling bipartite graphs with community structure"):
  with members:
    - "a scalable generative graph model with community structure"
    - "community structure and scale free collections of erdős renyi graphs"
    - "a scalable null model for directed graphs matching all degree distributions in out and reciprocal"

Connected through hyperedge HE-2 (induced by source paper "additive approximation algorithms for modularity maximization"):
  with members:
    - "network clustering via maximizing modularity approximation algorithms and theoretical limits"

Connected through hyperedge HE-3 (induced by source paper "quantitative function and algorithm for community detection in bipartite networks"):
  with members:
    - "local community extraction in directed networks"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "efficiently inferring community structure in bipartite networks"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"
    - "discovering link communities in complex networks by exploiting link dynamics"
    - "scalable spectral algorithms for community detection in directed networks"

Question: Please tell me which class the center node 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)