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 "pros and cons of gan evaluation measures"):
  Abstract: Generative models, in particular generative adverserial networks (GANs), have received a lot of attention recently. A number of GAN variants have been proposed and have been utilized in many applications. Despite large strides in terms of theoretical progress, evaluating and comparing GANs remains a daunting task. While several measures have been introduced, as of yet, there is no consensus as to which measure best captures strengths and limitations of models and should be used for fair model comparison. As in other areas of computer vision and machine learning, it is critical to settle on one or few good measures to steer the progress in this field. In this paper, I review and critically discuss more than 19 quantitative and 4 qualitative measures for evaluating generative models with a particular emphasis on GAN-derived models.

Includes members:
  Member "decoupled learning for conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised end to end learning of discrete linguistic units for voice conversion")
  Member "cross view image synthesis using conditional gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "generating adversarial examples with adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "network dissection quantifying interpretability of deep visual representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "gang of gans generative adversarial networks with maximum margin ranking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the evaluation of conditional gans")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "sampling generative networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "d vae a variational autoencoder for directed acyclic graphs")

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.CV(Computer Vision and Pattern Recognition)