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 "a convex duality framework for gans"):
  Abstract: Generative adversarial network (GAN) is a minimax game between a generator mimicking the true model and a discriminator distinguishing the samples produced by the generator from the real training samples. Given an unconstrained discriminator able to approximate any function, this game reduces to finding the generative model minimizing a divergence measure, e.g. the Jensen-Shannon (JS) divergence, to the data distribution. However, in practice the discriminator is constrained to be in a smaller class $\mathcal{F}$ such as neural nets. Then, a natural question is how the divergence minimization interpretation changes as we constrain $\mathcal{F}$. In this work, we address this question by developing a convex duality framework for analyzing GANs. For a convex set $\mathcal{F}$, this duality framework interprets the original GAN formulation as finding the generative model with minimum JS-divergence to the distributions penalized to match the moments of the data distribution, with the moments specified by the discriminators in $\mathcal{F}$. We show that this interpretation more generally holds for f-GAN and Wasserstein GAN. As a byproduct, we apply the duality framework to a hybrid of f-divergence and Wasserstein distance. Unlike the f-divergence, we prove that the proposed hybrid divergence changes continuously with the generative model, which suggests regularizing the discriminator's Lipschitz constant in f-GAN and vanilla GAN. We numerically evaluate the power of the suggested regularization schemes for improving GAN's training performance.

Includes members:
  Member "lipschitz margin training scalable certification of perturbation invariance for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the global convergence of actor critic a case for linear quadratic regulator with ergodic cost")
  Member "solving approximate wasserstein gans to stationarity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deconstructing generative adversarial networks")
  Member "a classification based study of covariate shift in gan distributions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "classification accuracy score for conditional generative models")
  Member "the numerics of gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "approximation and convergence properties of generative adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "a novel framework for selection of gans for an application")
  Member "f gans in an information geometric nutshell":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "connecting gans and mfgs")

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)