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 "exponential family graph embeddings"):
  Abstract: Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional \textit{Skip-Gram} model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic \textit{exponential family graph embedding} model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods 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 "kernel node embeddings":
    also connected through other hyperedges:
      (none)
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "tne a latent model for representation learning on networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "kernel node embeddings")
  Member "network embedding as matrix factorization unifying deepwalk line pte and node2vec":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "kernel node embeddings")
  Member "verse versatile graph embeddings from similarity measures":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph ordering towards the optimal by learning")

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.LG(Machine Learning)