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 "bipartite graph neural networks for efficient node representation learning"):
  Abstract: Existing Graph Neural Networks (GNNs) mainly focus on general structures, while the specific architecture on bipartite graphs---a crucial practical data form that consists of two distinct domains of nodes---is seldom studied. In this paper, we propose Bipartite Graph Neural Network (BGNN), a novel model that is domain-consistent, unsupervised, and efficient. At its core, BGNN utilizes the proposed Inter-domain Message Passing (IDMP) for message aggregation and Intra-domain Alignment (IDA) towards information fusion over domains, both of which are trained without requiring any supervision. Moreover, we formulate a multi-layer BGNN in a cascaded manner to enable multi-hop relation modeling while enjoying promising efficiency in training. Extensive experiments on several datasets of varying scales verify the effectiveness of BGNN compared to other counterparts. Particularly for the experiment on a large-scale bipartite graph dataset, the scalability of our BGNN is validated in terms of fast training speed and low memory cost.

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 "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can graph neural networks count substructures")
  Member "graph r cnn for scene graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deepinf social influence prediction with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "adaptive sampling towards fast graph representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral graph attention network")
  Member "hierarchical bipartite graph convolution networks":
    also connected through other hyperedges:
      (none)
  Member "fastgcn fast learning with graph convolutional networks via importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spectral graph attention network")
  Member "attributed social network embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep collaborative embedding for information cascade prediction")

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)