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: "overlapping community detection in complex networks using symmetric binary matrix factorization"
  Abstract: Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a modified partition density to evaluate the quality of community structures. We use this to determine the most appropriate number of communities. We evaluate our methods using both synthetic benchmarks and real-world networks, demonstrating the effectiveness of our approach.

Connected through hyperedge HE-1 (induced by source paper "identification of overlapping communities via constrained egonet tensor decomposition"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "a tensor approach to learning mixed membership community models"
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "online tensor methods for learning latent variable models"
    - "community detection in networks with node attributes"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (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"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "overlapping community detection in networks with positive and negative links"
    - "efficiently inferring community structure in bipartite networks"
    - "spectral redemption in clustering sparse networks"

Connected through hyperedge HE-3 (induced by source paper "community detection in complex networks using density based clustering algorithm and manifold learning"):
  with members:
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"
    - "mixing local and global information for community detection in large networks"

Connected through hyperedge HE-4 (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"
    - "community detection in bipartite networks using weighted symmetric binary matrix factorization"

Connected through hyperedge HE-5 (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"
    - "maximizing barber s bipartite modularity is also hard"
    - "efficiently inferring community structure in bipartite networks"
    - "discovering link communities in complex networks by exploiting link dynamics"
    - "scalable spectral algorithms for community detection in directed networks"

Connected through hyperedge HE-6 (induced by source paper "community detection in bipartite networks using weighted symmetric binary matrix factorization"):
  with members:
    - "community structure detection in complex networks with partial background information"
    - "virality prediction and community structure in social 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)