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: "approximation and convergence properties of generative adversarial learning"
  Abstract: Generative adversarial networks (GAN) approximate a target data distribution by jointly optimizing an objective function through a "two-player game" between a generator and a discriminator. Despite their empirical success, however, two very basic questions on how well they can approximate the target distribution remain unanswered. First, it is not known how restricting the discriminator family affects the approximation quality. Second, while a number of different objective functions have been proposed, we do not understand when convergence to the global minima of the objective function leads to convergence to the target distribution under various notions of distributional convergence. #R##N#In this paper, we address these questions in a broad and unified setting by defining a notion of adversarial divergences that includes a number of recently proposed objective functions. We show that if the objective function is an adversarial divergence with some additional conditions, then using a restricted discriminator family has a moment-matching effect. Additionally, we show that for objective functions that are strict adversarial divergences, convergence in the objective function implies weak convergence, thus generalizing previous results.

Connected through hyperedge HE-1 (induced by source paper "gans may have no nash equilibria"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "lipschitz generative adversarial nets"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "sgd learns one layer networks in wgans"
    - "on solving minimax optimization locally a follow the ridge approach"
    - "convergence of learning dynamics in stackelberg games"

Connected through hyperedge HE-2 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-3 (induced by source paper "smoothness and stability in gans"):
  with members:
    - "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"
    - "beyond local nash equilibria for adversarial networks"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-4 (induced by source paper "credible sample elicitation"):
  with members:
    - "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"
    - "water from two rocks maximizing the mutual information"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "density functional estimators with k nearest neighbor bandwidths"
    - "improved training of wasserstein gans"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"

Connected through hyperedge HE-5 (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"
    - "solving approximate wasserstein gans to stationarity"

Connected through hyperedge HE-6 (induced by source paper "bridging the gap between f gans and wasserstein gans"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "learning implicit generative models with the method of learned moments"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the inductive bias of restricted f gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "lipschitz generative adversarial nets"):
  with members:
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "which training methods for gans do actually converge"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "progressive growing of gans for improved quality stability and variation"
    - "the numerics of gans"

Connected through hyperedge HE-8 (induced by source paper "generative imputation and stochastic prediction"):
  with members:
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "nutrition and health data for cost sensitive learning"
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "spectral normalization for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "deep residual learning for image recognition"

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)