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 "parametric adversarial divergences are good task losses for generative modeling"):
  Abstract: Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem. In particular, how to evaluate a learned generative model is unclear. In this paper, we argue that *adversarial learning*, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such as for generating "visually realistic" images. By relating GANs and structured prediction under the framework of statistical decision theory, we put into light links between recent advances in structured prediction theory and the choice of the divergence in GANs. We argue that the insights about the notions of "hard" and "easy" to learn losses can be analogously extended to adversarial divergences. We also discuss the attractive properties of parametric adversarial divergences for generative modeling, and perform experiments to show the importance of choosing a divergence that reflects the final task.

Includes members:
  Member "on convergence and stability of gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "the cramer distance as a solution to biased wasserstein gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "mcgan mean and covariance feature matching gan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial code learning for image generation")
  Member "comparison of maximum likelihood and gan based training of real nvps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards gan benchmarks which require generalization")
  Member "approximation and convergence properties of generative adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gans may have no nash equilibria")
  Member "stabilizing training of generative adversarial networks through regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a novel framework for selection of gans for an application")
  Member "mmd gan towards deeper understanding of moment matching network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "on structured prediction theory with calibrated convex surrogate losses":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "strategic prediction with latent aggregative games")

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)