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: "generative adversarial parallelization"
  Abstract: Generative Adversarial Networks (GAN) have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation. They have also been shown to be capable of generating convincing examples in limited domains, such as low-resolution images. However, they still prove difficult to train in practice and tend to ignore modes of the data generating distribution. Quantitatively capturing effects such as mode coverage and more generally the quality of the generative model still remain elusive. We propose Generative Adversarial Parallelization (GAP), a framework in which many GANs or their variants are trained simultaneously, exchanging their discriminators. This eliminates the tight coupling between a generator and discriminator, leading to improved convergence and improved coverage of modes. We also propose an improved variant of the recently proposed Generative Adversarial Metric and show how it can score individual GANs or their collections under the GAP model.

Connected through hyperedge HE-1 (induced by source paper "stabilizing generative adversarial network training a survey"):
  with members:
    - "generative adversarial networks a survey and taxonomy"
    - "multi objective training of generative adversarial networks with multiple discriminators"
    - "evaluating gans via duality"
    - "convergence problems with generative adversarial networks gans"
    - "the relativistic discriminator a key element missing from standard gan"
    - "evolutionary generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "on convergence and stability of gans"

Connected through hyperedge HE-2 (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-3 (induced by source paper "spatial evolutionary generative adversarial networks"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "evolutionary generative adversarial networks"
    - "towards distributed coevolutionary gans"
    - "lipizzaner a system that scales robust generative adversarial network training"
    - "dual discriminator generative adversarial nets"
    - "dual motion gan for future flow embedded video prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "quantitatively evaluating gans with divergences proposed for training"):
  with members:
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comparison of maximum likelihood and gan based training of real nvps"
    - "the numerics of gans"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-5 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "multi generator generative adversarial nets"
    - "revisiting distributed synchronous sgd"
    - "distributed deep learning on edge devices feasibility via adaptive compression"
    - "invertible conditional gans for image editing"
    - "generative multi adversarial networks"
    - "towards geo distributed machine learning"

Connected through hyperedge HE-6 (induced by source paper "beyond local nash equilibria for adversarial networks"):
  with members:
    - "evaluating gans via duality"
    - "generative adversarial networks an overview"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "fictitious gan training gans with historical models"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-7 (induced by source paper "approximability of discriminators implies diversity in gans"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "a classification based study of covariate shift in gan distributions"
    - "pacgan the power of two samples in generative adversarial networks"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "improved training of wasserstein gans"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "on the discrimination generalization tradeoff in gans"
    - "generalization and equilibrium in generative adversarial nets gans"

Connected through hyperedge HE-8 (induced by source paper "towards distributed coevolutionary gans"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "multi generator generative adversarial nets"
    - "es is more than just a traditional finite difference approximator"
    - "improved training of wasserstein gans"
    - "do gans actually learn the distribution an empirical study"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "generative multi adversarial networks"
    - "nips 2016 tutorial generative adversarial networks"

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)