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: "joint embedding of meta path and meta graph for heterogeneous information networks"
  Abstract: Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks,where a meta-graph is a composition of meta-paths that captures the complex structural information. However, current relevance computing based on meta-graph only considers the complex structural information, but ignores its embedded meta-paths information. To address this problem, we proposeMEta-GrAph-based network embedding models, called MEGA and MEGA++, respectively. The MEGA model uses normalized relevance or similarity measures that are derived from a meta-graph and its embedded meta-paths between nodes simultaneously, and then leverages tensor decomposition method to perform node embedding. The MEGA++ further facilitates the use of coupled tensor-matrix decomposition method to obtain a joint embedding for nodes, which simultaneously considers the hidden relations of all meta information of a meta-graph.Extensive experiments on two real datasets demonstrate thatMEGA and MEGA++ are more effective than state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous graph attention network"):
  with members:
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "embedding logical queries on knowledge graphs"
    - "graph embedding techniques applications and performance a survey"
    - "attention is all you need"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for recommendation"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-2 (induced by source paper "hetespaceywalk a heterogeneous spacey random walk for heterogeneous information network embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "carl content aware representation learning for heterogeneous networks"
    - "the spacey random walk a stochastic process for higher order data"
    - "heterogeneous information network embedding for recommendation"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "meta path guided embedding for similarity search in large scale heterogeneous information networks"

Connected through hyperedge HE-3 (induced by source paper "a self organizing tensor architecture for multi view clustering"):
  with members:
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "a survey on multi view clustering"
    - "online unsupervised multi view feature selection"

Connected through hyperedge HE-4 (induced by source paper "unsupervised meta path reduction on heterogeneous information networks"):
  with members:
    - "unsupervised feature selection with adaptive structure learning"
    - "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)