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: "generalized communities in networks"
  Abstract: A substantial volume of research has been devoted to studies of community structure in networks, but communities are not the only possible form of large-scale network structure. Here we describe a broad extension of community structure that encompasses traditional communities but includes a wide range of generalized structural patterns as well. We describe a principled method for detecting this generalized structure in empirical network data and demonstrate with real-world examples how it can be used to learn new things about the shape and meaning of networks.

Connected through hyperedge HE-1 (induced by source paper "analysis of large sparse graphs using regular decomposition of graph distance matrices"):
  with members:
    - "regular decomposition an information and graph theoretic approach to stochastic block models"
    - "moment based parameter estimation in binomial random intersection graph models"
    - "diclique clustering in a directed random graph"
    - "multilabel classification through random graph ensembles"

Connected through hyperedge HE-2 (induced by source paper "reconstructing networks with unknown and heterogeneous errors"):
  with members:
    - "network structure from rich but noisy data"
    - "structural inference for uncertain networks"
    - "structure and inference in annotated networks"
    - "discovering social circles in ego networks"

Connected through hyperedge HE-3 (induced by source paper "from relational data to graphs inferring significant links using generalized hypergeometric ensembles"):
  with members:
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "the essential role of time in network based recommendation"
    - "a testing based extraction algorithm for identifying significant communities in networks"

Connected through hyperedge HE-4 (induced by source paper "high degree vertices and spread of infections in spatially modelled social networks"):
  with members:
    - "rumours spread slowly in a small world spatial network"
    - "geometric graph properties of the spatial preferred attachment model"

Connected through hyperedge HE-5 (induced by source paper "a framework for the construction of generative models for mesoscale structure in multilayer networks"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "random graph models for dynamic networks"
    - "the many facets of community detection in complex networks"
    - "the geometric block model"
    - "block models and personalized pagerank"
    - "community detection link prediction and layer interdependence in multilayer networks"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in 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)