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: "clustering attributed graphs models measures and methods"
  Abstract: Clustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models as attributed graphs. Consequently, existing graph clustering methods have been recently extended to deal with node and edge attributes. This article is a literature survey on this topic, organizing and presenting recent research results in a uniform way, characterizing the main existing clustering methods and highlighting their conceptual differences. We also cover the important topic of clustering evaluation and identify current open problems.

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 "clustering in graphs and hypergraphs with categorical edge labels"):
  with members:
    - "nonlinearity networks a 2020 vision"
    - "a framework for second order eigenvector centralities and clustering coefficients"
    - "solving linear programs in the current matrix multiplication time"
    - "three hypergraph eigenvector centralities"
    - "motif and hypergraph correlation clustering"
    - "simplicial closure and higher order link prediction"
    - "correlation clustering generalized"
    - "spectral sparsification of simplicial complexes for clustering and label propagation"

Connected through hyperedge HE-3 (induced by source paper "community detection in multiplex networks"):
  with members:
    - "overlapping community detection by local decentralised vertex centred process"
    - "node centric community detection in multilayer networks with layer coverage diversification bias"
    - "mapping higher order network flows in memory and multilayer networks with infomap"
    - "community detection in multiplex networks using locally adaptive random walks"
    - "a local perspective on community structure in multilayer networks"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "efficient community detection in large networks using content and links"

Connected through hyperedge HE-4 (induced by source paper "an analysis of the consequences of the general data protection regulation gdpr on social network research"):
  with members:
    - "foundations of temporal text networks"
    - "a classification for community discovery methods in complex networks"

Connected through hyperedge HE-5 (induced by source paper "foundations of temporal text networks"):
  with members:
    - "detecting socio economic impact of cultural investment through geo social network analysis"
    - "graph embedding techniques applications and performance a survey"
    - "motifs in temporal networks"
    - "automated analysis of topic actor networks on twitter new approach to the analysis of socio semantic networks"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "modelling sequences and temporal networks with dynamic community structures"
    - "do cascades recur"
    - "spreading processes in multilayer networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "infinite multiple membership relational modeling for complex networks"

Connected through hyperedge HE-7 (induced by source paper "contextual stochastic block models"):
  with members:
    - "algorithmic decision making and the cost of fairness"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "partitioning networks with node attributes by compressing information flow"
    - "community detection in networks with node attributes"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "latent multi group membership graph model"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-8 (induced by source paper "stochastic block models with multiple continuous attributes"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "structure and inference in annotated networks"
    - "link prediction in social networks the state of the art"
    - "producing a unified graph representation from multiple social network views"
    - "community detection in networks with node attributes"
    - "defining and evaluating network communities based on ground truth"
    - "comparative study for inference of hidden classes in stochastic block models"
    - "empirical comparison of algorithms for network community detection"

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)