human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "enhancing network embedding with auxiliary information an explicit matrix factorization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monet debiasing graph embeddings via the metadata orthogonal training unit")
  Member "attributed network embedding for learning in a dynamic environment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "a comprehensive survey of graph embedding problems techniques and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "representation learning for scale free networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hyperbolic multiplex network embedding with maps of random walk")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep graph matching consensus")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "revisiting semi supervised learning with graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral graph attention network")

Question: Please tell me which class the source paper 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)