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: "community detection in bipartite networks using weighted symmetric binary matrix factorization"
  Abstract: In this paper, we propose weighted symmetric binary matrix factorization (wSBMF) framework to detect overlapping communities in bipartite networks, which describes the relationships between two types of nodes. Our method improves performance by recognizing the distinction between two types of missing edges — ones among the nodes in each node type and the others between two node types. Our method can also explicitly assign community membership and distinguish outliers from overlapping nodes, as well as incorporating existing knowledge on the network. We propose a generalized partition density for bipartite networks as a quality function, which identifies the most appropriate number of communities. The experimental results on both synthetic and real-world networks demonstrate the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "on equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization"):
  with members:
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "overlapping community detection in networks with positive and negative links"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse networks"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "community detection in complex networks using density based clustering algorithm and manifold learning"):
  with members:
    - "mixing local and global information for community detection in large networks"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "community detection in complex networks using link prediction"):
  with members:
    - "community detection in complex networks using density based clustering algorithm and manifold learning"
    - "overlapping community detection in complex networks using symmetric binary matrix factorization"

Connected through hyperedge HE-4 (induced by source paper "quantitative function and algorithm for community detection in bipartite networks"):
  with members:
    - "local community extraction in directed networks"
    - "maximizing barber s bipartite modularity is also hard"
    - "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)