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 "kernel node embeddings"):
  Abstract: Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classification. Two popular approaches for this problem include matrix factorization and random walk-based models. In this paper, we aim to bring together the best of both worlds, towards learning latent node representations. In particular, we propose a weighted matrix factorization model which encodes random walk-based information about the nodes of the graph. The main benefit of this formulation is that it allows to utilize kernel functions on the computation of the embeddings. We perform an empirical evaluation on real-world networks, showing that the proposed model outperforms baseline node embedding algorithms in two downstream machine learning tasks.

Includes members:
  Member "is a single embedding enough learning node representations that capture multiple social contexts":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph neighborhood attentive pooling")
  Member "netsmf large scale network embedding as sparse matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank")
  Member "tne a latent model for representation learning on networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exponential family graph embeddings")
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "biasedwalk biased sampling for representation learning on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exponential family graph embeddings")
  Member "network representation learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches")
  Member "harp hierarchical representation learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization")
  Member "representation learning on graphs methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to segment 3d point clouds in 2d image space")

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)