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: "metagraph2vec complex semantic path augmented heterogeneous network embedding"
  Abstract: Network embedding in heterogeneous information networks (HINs) is a challenging task, due to complications of different node types and rich relationships between nodes. As a result, conventional network embedding techniques cannot work on such HINs. Recently, metapath-based approaches have been proposed to characterize relationships in HINs, but they are ineffective in capturing rich contexts and semantics between nodes for embedding learning, mainly because (1) metapath is a rather strict single path node-node relationship descriptor, which is unable to accommodate variance in relationships, and (2) only a small portion of paths can match the metapath, resulting in sparse context information for embedding learning. In this paper, we advocate a new metagraph concept to capture richer structural contexts and semantics between distant nodes. A metagraph contains multiple paths between nodes, each describing one type of relationships, so the augmentation of multiple metapaths provides an effective way to capture rich contexts and semantic relations between nodes. This greatly boosts the ability of metapath-based embedding techniques in handling very sparse HINs. We propose a new embedding learning algorithm, namely MetaGraph2Vec, which uses metagraph to guide the generation of random walks and to learn latent embeddings of multi-typed HIN nodes. Experimental results show that MetaGraph2Vec is able to outperform the state-of-the-art baselines in various heterogeneous network mining tasks such as node classification, node clustering, and similarity search.

Connected through hyperedge HE-1 (induced by source paper "task guided pair embedding in heterogeneous network"):
  with members:
    - "link prediction based on graph neural networks"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "capturing edge attributes via network embedding"
    - "an attention based collaboration framework for multi view network representation learning"
    - "advances in pre training distributed word representations"
    - "heterogeneous information network embedding for recommendation"
    - "effectiveness of anonymization in double blind review"

Connected through hyperedge HE-2 (induced by source paper "hahe hierarchical attentive heterogeneous information network embedding"):
  with members:
    - "hierarchical graph representation learning with differentiable pooling"
    - "attention models in graphs a survey"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "heterogeneous information network embedding for recommendation"
    - "attributed network embedding for learning in a dynamic environment"
    - "heterogeneous information network embedding for meta path based proximity"

Connected through hyperedge HE-3 (induced by source paper "motif2vec motif aware node representation learning for heterogeneous networks"):
  with members:
    - "graph embedding techniques applications and performance a survey"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "harp hierarchical representation learning for networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (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"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "carl content aware representation learning for heterogeneous networks"
    - "joint embedding of meta path and meta graph for heterogeneous information 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-5 (induced by source paper "higher order clustering in heterogeneous information networks"):
  with members:
    - "higher order clustering in networks"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "simplicial closure and higher order link prediction"
    - "motifs in temporal networks"
    - "recurrent meta structure for robust similarity measure in heterogeneous information networks"
    - "motif based convolutional neural network on graphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"

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)