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 "bayesian graph convolutional neural networks using node copying"):
  Abstract: Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks. Although the techniques obtain impressive results, they often fall short in accounting for the uncertainty associated with the underlying graph structure. In the recently proposed Bayesian GCNN (BGCN) framework, this issue is tackled by viewing the observed graph as a sample from a parametric random graph model and targeting joint inference of the graph and the GCNN weights. In this paper, we introduce an alternative generative model for graphs based on copying nodes and incorporate it within the BGCN framework. Our approach has the benefit that it uses information provided by the node features and training labels in the graph topology inference. Experiments show that the proposed algorithm compares favorably to the state-of-the-art in benchmark node classification tasks.

Includes members:
  Member "fisher bures adversary graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relation embedding for personalised poi recommendation")
  Member "residual gated graph convnets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking graph neural networks")
  Member "dual primal graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable graph module dgm graph convolutional networks")
  Member "fastgcn fast learning with graph convolutional networks via importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral graph attention network")
  Member "dynamic edge conditioned filters in convolutional neural networks on graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "memory based graph networks")
  Member "random graph models for dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models")
  Member "robust spatial filtering with graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive fractional order graph neural network")
  Member "cayleynets graph convolutional neural networks with complex rational spectral filters":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")

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)