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 "generalizing graph convolutional neural networks with edge variant recursions on graphs"):
  Abstract: This paper reviews graph convolutional neural networks (GCNNs) through the lens of edge-variant graph filters. The edge-variant graph filter is a finite order, linear, and local recursion that allows each node, in each iteration, to weigh differently the information of its neighbors. By exploiting this recursion, we formulate a general framework for GCNNs which considers state-of-the-art solutions as particular cases. This framework results useful to i) understand the tradeoff between local detail and the number of parameters of each solution and ii) provide guidelines for developing a myriad of novel approaches that can be implemented locally in the vertex domain. One of such approaches is presented here showing superior performance w.r.t. current alternatives in graph signal classification problems.

Includes members:
  Member "graph neural networks with convolutional arma filters":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "topology adaptive graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image")
  Member "median activation functions for graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graphon pooling in graph neural networks")
  Member "autoregressive moving average graph filtering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  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 "convolutional neural networks via node varying graph filters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "edgenets edge varying graph neural networks")
  Member "cayleynets graph convolutional neural networks with complex rational spectral filters":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "efficient sampling set selection for bandlimited graph signals using graph spectral proxies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "edgenets edge varying 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)