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: "discovering social circles in ego networks"
  Abstract: People's personal social networks are big and cluttered, and currently there is no good way to automatically organize them. Social networking sites allow users to manually categorize their friends into social circles (e.g. 'circles' on Google+, and 'lists' on Facebook and Twitter), however they are laborious to construct and must be updated whenever a user's network grows. In this paper, we study the novel task of automatically identifying users' social circles. We pose this task as a multi-membership node clustering problem on a user's ego-network, a network of connections between her friends. We develop a model for detecting circles that combines network structure as well as user profile information. For each circle we learn its members and the circle-specific user profile similarity metric. Modeling node membership to multiple circles allows us to detect overlapping as well as hierarchically nested circles. Experiments show that our model accurately identifies circles on a diverse set of data from Facebook, Google+, and Twitter, for all of which we obtain hand-labeled ground-truth.

Connected through hyperedge HE-1 (induced by source paper "relational thematic clustering with mutually preferred neighbors"):
  with members:
    - "gromov wasserstein factorization models for graph clustering"
    - "community detection in networks with node attributes"

Connected through hyperedge HE-2 (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-3 (induced by source paper "vgraph a generative model for joint community detection and node representation learning"):
  with members:
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "communitygan community detection with generative adversarial nets"
    - "verse versatile graph embeddings from similarity measures"
    - "gemsec graph embedding with self clustering"
    - "a unified framework for community detection and network representation learning"
    - "node2vec scalable feature learning for networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "line large scale information network embedding"

Connected through hyperedge HE-4 (induced by source paper "modeling and analysis of tagging networks in stack exchange communities"):
  with members:
    - "simplicial closure and higher order link prediction"
    - "choosing to grow a graph modeling network formation as discrete choice"
    - "motifs in temporal networks"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "who is authoritative understanding reputation mechanisms in quora"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"
    - "collective dynamics of social annotation"

Connected through hyperedge HE-5 (induced by source paper "conditional adjacency anonymity in social graphs under active attacks"):
  with members:
    - "on the computational complexities of three privacy measures for large networks under active attack"
    - "preserving link privacy in social network based systems"
    - "de anonymizing social networks"
    - "anonymity and information hiding in multiagent systems"

Connected through hyperedge HE-6 (induced by source paper "parameter free structural diversity search"):
  with members:
    - "a fast order based approach for core maintenance"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-7 (induced by source paper "overlapping community detection with graph neural networks"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "verse versatile graph embeddings from similarity measures"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "ensemble based overlapping community detection using disjoint community structures"):
  with members:
    - "dyperm maximizing permanence for dynamic community detection"
    - "iedc an integrated approach for overlapping and non overlapping community detection"
    - "ensemble based algorithms to detect disjoint and overlapping communities in networks"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "on the formation of circles in co authorship networks"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "overlapping community detection in networks the state of the art and comparative study"

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)