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: "spine structural identity preserved inductive network embedding"
  Abstract: Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embedding methods encode the local proximity of a node, such as the first- and second-order proximities. While being efficient, these methods are short of leveraging the global structural information between nodes distant from each other. In addition, most existing methods learn embeddings on one single fixed network, and thus cannot be generalized to unseen nodes or networks without retraining. In this paper we present SPINE, a method that can jointly capture the local proximity and proximities at any distance, while being inductive to efficiently deal with unseen nodes or networks. Specifically, we identify the structural identity of each node based on a high-order proximity of the network named Rooted PageRank, followed by a novel biased Skip-Gram Negative Sampling algorithm and a carefully designed objective function to encode local proximities and global structural identities of nodes simultaneously. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.

Connected through hyperedge HE-1 (induced by source paper "deep representation learning for social network analysis"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "hierarchical graph representation learning with differentiable pooling"
    - "techniques for interpretable machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "attributed social network embedding"
    - "representation learning on graphs methods and applications"
    - "heterogeneous information network embedding for recommendation"

Connected through hyperedge HE-2 (induced by source paper "a capsule network based model for learning node embeddings"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "a tutorial on network embeddings"
    - "relational inductive biases deep learning and graph networks"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "network representation learning a survey"
    - "dynamic routing between capsules"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-3 (induced by source paper "dynamic embedding on textual networks via a gaussian process"):
  with members:
    - "graph neural networks for social recommendation"
    - "graph convolutional neural networks for web scale recommender systems"
    - "nscaching simple and efficient negative sampling for knowledge graph embedding"
    - "dyngem deep embedding method for dynamic graphs"
    - "improved semantic aware network embedding with fine grained word alignment"
    - "diffusion maps for textual network embedding"
    - "representation learning over dynamic graphs"
    - "predicting multicellular function through multi layer tissue networks"

Connected through hyperedge HE-4 (induced by source paper "attention models in graphs a survey"):
  with members:
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"
    - "treegan syntax aware sequence generation with generative adversarial networks"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "recurrent neural network attention mechanisms for interpretable system log anomaly detection"
    - "an attention based collaboration framework for multi view network representation learning"
    - "on sampling from massive graph streams"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "enhancing network embedding with auxiliary information an explicit matrix factorization perspective"):
  with members:
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "line large scale information network embedding"
    - "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)