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 "solving approximate wasserstein gans to stationarity"):
  Abstract: Generative Adversarial Networks (GANs) are one of the most practical strategies to learn data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions. Unfortunately, minimizing the Wasserstein distance between the data distribution and the generative model distribution is a challenging problem as its objective is non-convex, non-smooth, and even hard to compute. In this work, we propose to use a smooth approximation of the Wasserstein GANs. We show that this smooth approximation is close to the original objective. Moreover, obtaining gradient information of this approximate formulation is computationally effortless and hence one can easily apply first order optimization methods to optimize this objective. Based on this observation, we proposed a class of algorithms with guaranteed theoretical convergence to stationarity. Unlike the original non-smooth objective, our proposed algorithm only requires solving the discriminator to approximate optimality. We applied our method to learning Gaussian mixtures on a grid and also to learning MNIST digits. Our method allows the use of powerful cost functions based on latent representations of the data, where this latent representation could also be optimized adversarially.

Includes members:
  Member "improving gans using optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  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 "which training methods for gans do actually converge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel framework for selection of gans for an application")
  Member "gans trained by a two time scale update rule converge to a nash equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "near linear time approximation algorithms for optimal transport via sinkhorn iteration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric dataset distances via optimal transport")
  Member "the numerics of gans":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when relation networks meet gans relation gans with triplet loss")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "gradient descent gan optimization is locally stable":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial code learning for image generation")

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)