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 using neighborhood inflated seed expansion"
  Abstract: Community detection is an important task in network analysis. A community (also referred to as a cluster) is a set of cohesive vertices that have more connections inside the set than outside. In many social and information networks, these communities naturally overlap. For instance, in a social network, each vertex in a graph corresponds to an individual who usually participates in multiple communities. In this paper, we propose an efficient overlapping community detection algorithm using a seed expansion approach. The key idea of our algorithm is to find good seeds, and then greedily expand these seeds based on a community metric. Within this seed expansion method, we investigate the problem of how to determine good seed nodes in a graph. In particular, we develop new seeding strategies for a personalized PageRank clustering scheme that optimizes the conductance community score. Experimental results show that our seed expansion algorithm outperforms other state-of-the-art overlapping community detection methods in terms of producing cohesive clusters and identifying ground-truth communities. We also show that our new seeding strategies are better than existing strategies, and are thus effective in finding good overlapping communities in real-world networks.

Connected through hyperedge HE-1 (induced by source paper "community detection in node attributed social networks a survey"):
  with members:
    - "attributed graph clustering a deep attentional embedding approach"
    - "non linear attributed graph clustering by symmetric nmf with pu learning"
    - "efficiently clustering very large attributed graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "detection of community structures in networks with nodal features based on generative probabilistic approach"):
  with members:
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "general optimization technique for high quality community detection in complex networks"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "relation strength aware clustering of heterogeneous information networks with incomplete attributes"
    - "social structure of facebook networks"

Connected through hyperedge HE-3 (induced by source paper "community detection in partially observable social networks"):
  with members:
    - "provable and practical approximations for the degree distribution using sublinear graph samples"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "community detection in networks with node attributes"
    - "estimation of simultaneously sparse and low rank matrices"
    - "structure and overlaps of communities in networks"
    - "defining and evaluating network communities based on ground truth"
    - "finding missing edges and communities in incomplete networks"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"

Connected through hyperedge HE-4 (induced by source paper "overlapping community detection using superior seed set selection in social networks"):
  with members:
    - "integrated structure investigation in complex networks by label propagation"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-5 (induced by source paper "krylov subspace approximation for local community detection in large networks"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "hidden community detection in social networks"
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "detecting overlapping communities from local spectral subspaces"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"

Connected through hyperedge HE-6 (induced by source paper "iedc an integrated approach for overlapping and non overlapping community detection"):
  with members:
    - "memory enriched big bang big crunch optimization algorithm for data clustering"
    - "relevant based structure learning for feature selection"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "general optimization technique for high quality community detection in complex networks"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "social structure of facebook networks"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "detecting highly cyclic structure with complex eigenpairs"):
  with members:
    - "tensor spectral clustering for partitioning higher order network structures"
    - "using triangles to improve community detection in directed networks"
    - "finding role communities in directed networks using role based similarity markov stability and the relaxed minimum spanning tree"
    - "clustering and community detection in directed networks a survey"
    - "triadic measures on graphs the power of wedge sampling"
    - "directed closure measures for networks with reciprocity"
    - "scalable spectral algorithms for community detection in directed networks"

Connected through hyperedge HE-8 (induced by source paper "a local perspective on community structure in multilayer networks"):
  with members:
    - "block models and personalized pagerank"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "spreading processes in multilayer networks"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "seeded pagerank solution paths"
    - "pagerank beyond the web"
    - "ranking and clustering of nodes in networks with smart teleportation"
    - "defining and evaluating network communities based on ground truth"

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)