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: "shaping communities out of triangles"
  Abstract: Community detection has arisen as one of the most relevant topics in the field of graph data mining due to its importance in many fields such as biology, social networks or network traffic analysis. The metrics proposed to shape communities are generic and follow two approaches: maximizing the internal density of such communities or reducing the connectivity of the internal vertices with those outside the community. However, these metrics take the edges as a set and do not consider the internal layout of the edges in the community. We define a set of properties oriented to social networks that ensure that communities are cohesive, structured and well defined. Then, we propose the Weighted Community Clustering (WCC), which is a community metric based on triangles. We proof that analyzing communities by triangles gives communities that fulfill the listed set of properties, in contrast to previous metrics. Finally, we experimentally show that WCC correctly captures the concept of community in social networks using real and syntethic datasets, and compare statistically some of the most relevant community detection algorithms in the state of the art.

Connected through hyperedge HE-1 (induced by source paper "node centric detection of overlapping communities in social networks"):
  with members:
    - "extension of modularity density for overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"
    - "towards linear time overlapping community detection in social networks"
    - "defining and evaluating network communities based on ground truth"
    - "detect overlapping and hierarchical community structure in networks"
    - "on the np completeness of some graph cluster measures"

Connected through hyperedge HE-2 (induced by source paper "query expansion via structural motifs in wikipedia graph"):
  with members:
    - "understanding graph structure of wikipedia for query expansion"

Connected through hyperedge HE-3 (induced by source paper "tensor spectral clustering for partitioning higher order network structures"):
  with members:
    - "the spacey random walk a stochastic process for higher order data"
    - "using triangles to improve community detection in directed networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "clustering and community detection in directed networks a survey"
    - "tensor decompositions for learning latent variable models"
    - "degree relations of triangles in real world networks and graph models"
    - "mutual or unrequited love identifying stable clusters in social networks with uni and bi directional links"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "socially aware distributed hash tables for decentralized online social networks"):
  with members:
    - "special issue on foundations of social computing enabling social applications via decentralized social data management"
    - "decent a decentralized architecture for enforcing privacy in online social networks"
    - "supernova super peers based architecture for decentralized online social networks"

Connected through hyperedge HE-5 (induced by source paper "using triangles to improve community detection in directed networks"):
  with members:
    - "clustering and community detection in directed networks a survey"
    - "directed closure measures for networks with reciprocity"
    - "community structure and scale free collections of erdős renyi graphs"
    - "mutual or unrequited love identifying stable clusters in social networks with uni and bi directional links"

Connected through hyperedge HE-6 (induced by source paper "distributed community detection with the wcc metric"):
  with members:
    - "parallel heuristics for scalable community detection"
    - "graph spectra and the detectability of community structure in networks"
    - "towards linear time overlapping community detection 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)