human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "deconstructing generative adversarial networks"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "stabilizing gan training with multiple random projections"
    - "approximability of discriminators implies diversity in gans"
    - "a convex duality framework for gans"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on the implicit assumptions of gans"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial computation of optimal transport maps"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "spectral normalization for generative adversarial networks"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "improving gans using optimal transport"
    - "on convergence and stability of gans"
    - "guiding the one to one mapping in cyclegan via optimal transport"
    - "pacgan the power of two samples in generative adversarial networks"
    - "a geometric view of optimal transportation and generative model"

Connected through hyperedge HE-3 (induced by source paper "on gradient descent ascent for nonconvex concave minimax problems"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "local saddle point optimization a curvature exploitation approach"
    - "a variational inequality perspective on generative adversarial nets"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "an online learning approach to generative adversarial networks"
    - "the numerics of gans"

Connected through hyperedge HE-4 (induced by source paper "a convex duality framework for gans"):
  with members:
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "a classification based study of covariate shift in gan distributions"
    - "the numerics of gans"
    - "approximation and convergence properties of generative adversarial learning"
    - "stabilizing training of generative adversarial networks through regularization"
    - "f gans in an information geometric nutshell"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "entropic gans meet vaes a statistical approach to compute sample likelihoods in gans"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "on unifying deep generative models"
    - "improved training of wasserstein gans"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "to go or not to go a near unsupervised learning approach for robot navigation"
    - "learning a driving simulator"

Question: Please tell me which class the center node 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)