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 "multi hot compact network embedding"):
  Abstract: Network embedding, as a promising way of the network representation learning, is capable of supporting various subsequent network mining and analysis tasks, and has attracted growing research interests recently. Traditional approaches assign each node with an independent continuous vector, which will cause huge memory overhead for large networks. In this paper we propose a novel multi-hot compact embedding strategy to effectively reduce memory cost by learning partially shared embeddings. The insight is that a node embedding vector is composed of several basis vectors, which can significantly reduce the number of continuous vectors while maintain similar data representation ability. Specifically, we propose a MCNE model to learn compact embeddings from pre-learned node features. A novel component named compressor is integrated into MCNE to tackle the challenge that popular back-propagation optimization cannot propagate through discrete samples. We further propose an end-to-end model MCNE$_{t}$ to learn compact embeddings from the input network directly. Empirically, we evaluate the proposed models over three real network datasets, and the results demonstrate that our proposals can save about 90\% of memory cost of network embeddings without significantly performance decline.

Includes members:
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "adversarially regularized graph autoencoder for graph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")
  Member "cosine compressive network embedding on large scale information networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphzoom a multi level spectral approach for accurate and scalable graph embedding")
  Member "network representation learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches")
  Member "struc2vec learning node representations from structural identity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cube2net efficient query specific network construction with data cube organization")
  Member "harp hierarchical representation learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization")
  Member "a survey on network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "graphgan graph representation learning with generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mga momentum gradient attack on 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)