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 "stabilizing generative adversarial network training a survey"):
  Abstract: Generative Adversarial Networks (GANs) are a type of Generative Models, which received wide attention for their ability to model complex real-world data. With a considerably large number of potential applications and a promise to alleviate some of the major issues of the field of Artificial Intelligence, recent years have seen rapid progress in the field of GANs. Despite the relative success, the process of training GANs remains challenging, suffering from instability issues such as mode collapse and vanishing gradients. This survey summarizes the approaches and methods employed for the purpose of stabilizing GAN training procedure. We provide a brief explanation behind the workings of GANs and categorize employed stabilization techniques outlining each of the approaches. Finally, we discuss the advantages and disadvantages of each of the methods, offering a comparative summary of the literature on stabilizing GAN training procedure.

Includes members:
  Member "generative adversarial networks a survey and taxonomy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel framework for selection of gans for an application")
  Member "multi objective training of generative adversarial networks with multiple discriminators":
    also connected through other hyperedges:
      (none)
  Member "evaluating gans via duality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond local nash equilibria for adversarial networks")
  Member "convergence problems with generative adversarial networks gans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalizing semi supervised generative adversarial networks to regression using feature contrasting")
  Member "the relativistic discriminator a key element missing from standard gan":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "evolutionary generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mdea malware detection with evolutionary adversarial learning")
  Member "large scale gan training for high fidelity natural image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "on convergence and stability of gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "blur noise and compression robust generative adversarial 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)