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: "infinite multiple membership relational modeling for complex networks"
  Abstract: Learning latent structure in complex networks has become an important problem fueled by many types of networked data originating from practically all fields of science. In this paper, we propose a new non-parametric Bayesian multiple-membership latent feature model for networks. Contrary to existing multiple-membership models that scale quadratically in the number of vertices the proposed model scales linearly in the number of links admitting multiple-membership analysis in large scale networks. We demonstrate a connection between the single membership relational model and multiple membership models and show on "real" size benchmark network data that accounting for multiple memberships improves the learning of latent structure as measured by link prediction while explicitly accounting for multiple membership result in a more compact representation of the latent structure of networks.

Connected through hyperedge HE-1 (induced by source paper "prioritizing network communities"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "higher order organization of complex networks"
    - "structure and inference in annotated networks"
    - "clustering attributed graphs models measures and methods"
    - "a tensor approach to learning mixed membership community models"
    - "distributed representations of words and phrases and their compositionality"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-2 (induced by source paper "a network model characterized by a latent attribute structure with competition"):
  with members:
    - "a weighted correlation index for rankings with ties"
    - "nonparametric link prediction in dynamic networks"
    - "an infinite latent attribute model for network data"
    - "hyperanf approximating the neighbourhood function of very large graphs on a budget"

Connected through hyperedge HE-3 (induced by source paper "community detection in networks with node attributes"):
  with members:
    - "overlapping community detection in networks the state of the art and comparative study"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "demon a local first discovery method for overlapping communities"
    - "structure and overlaps of communities in networks"
    - "latent multi group membership graph model"
    - "efficient community detection in large networks using content and links"
    - "neighborhoods are good communities"

Connected through hyperedge HE-4 (induced by source paper "nonparametric multi group membership model for dynamic networks"):
  with members:
    - "latent multi group membership graph model"
    - "an infinite latent attribute model for network data"
    - "modeling social networks with node attributes using the multiplicative attribute graph model"
    - "multiplicative attribute graph model of real world networks"
    - "supervised random walks predicting and recommending links 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)