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 "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding"):
  Abstract: Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general framework to scale graph AE and VAE to large graphs with millions of nodes and edges. Our strategy, based on node sampling and subgraph decoding, significantly speeds up the training of graph AE and VAE while preserving or even improving performances. We demonstrate the effectiveness of FastGAE on numerous real-world graphs, outperforming the few existing approaches to scale graph AE and VAE by a wide margin.

Includes members:
  Member "simple and effective graph autoencoders with one hop linear models":
    also connected through other hyperedges:
      (none)
  Member "gravity inspired graph autoencoders for directed link prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keep it simple graph autoencoders without graph convolutional networks")
  Member "a degeneracy framework for scalable graph autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "simple and effective graph autoencoders with one hop linear models")
  Member "effective decoding in graph auto encoder using triadic closure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "simple and effective graph autoencoders with one hop linear models")
  Member "cluster gcn an efficient algorithm for training deep and large graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ripple walk training a subgraph based training framework for large and deep graph neural network")
  Member "semi implicit graph variational auto encoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "drum end to end differentiable rule mining on knowledge graphs")
  Member "improved training speed accuracy and data utilization through loss function optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta learning curiosity algorithms")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on the expressive power of 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)