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: "capturing edge attributes via network embedding"
  Abstract: Network embedding, which aims to learn low-dimensional representations of nodes, has been used for various graph related tasks including visualization, link prediction, and node classification. Most existing embedding methods rely solely on network structure. However, in practice, we often have auxiliary information about the nodes and/or their interactions, e.g., the content of scientific papers in coauthorship networks, or topics of communication in Twitter mention networks. Here, we propose a novel embedding method that uses both network structure and edge attributes to learn better network representations. Our method jointly minimizes the reconstruction error for higher order node neighborhood, social roles, and edge attributes using a deep architecture that can adequately capture highly nonlinear interactions. We demonstrate the efficacy of our model over existing state-of-the-art methods on a variety of real-world networks including collaboration networks and social networks. We also observe that using edge attributes to inform network embedding yields better performance in downstream tasks such as link prediction and node classification.

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"
    - "metagraph2vec complex semantic path augmented heterogeneous network embedding"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "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 "dynamic joint variational graph autoencoders"):
  with members:
    - "dynamicgem a library for dynamic graph embedding methods"
    - "dyngem deep embedding method for dynamic graphs"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "learning dynamic embeddings from temporal interactions"
    - "representation learning over dynamic graphs"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-3 (induced by source paper "rwr gae random walk regularization for graph auto encoders"):
  with members:
    - "a survey on network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "deepwalk online learning of social representations"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

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)