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: "coulomb gans provably optimal nash equilibria via potential fields"
  Abstract: Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution. We introduce Coulomb GANs, which pose the GAN learning problem as a potential field of charged particles, where generated samples are attracted to training set samples but repel each other. The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field. Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes. We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution. We show the efficacy of Coulomb GANs on a variety of image datasets. On LSUN and celebA, Coulomb GANs set a new state of the art and produce a previously unseen variety of different samples.

Connected through hyperedge HE-1 (induced by source paper "microbatchgan stimulating diversity with multi adversarial discrimination"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "on convergence and stability of gans"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"
    - "mcgan mean and covariance feature matching gan"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "pacgan the power of two samples in generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "one big net for everything"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-3 (induced by source paper "multi adversarial variational autoencoder networks"):
  with members:
    - "learning implicit generative models with the method of learned moments"
    - "stabilizing gan training with multiple random projections"
    - "spectral normalization for generative adversarial networks"
    - "autoregressive quantile networks for generative modeling"
    - "learning implicit brain mri manifolds with deep learning"
    - "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification"
    - "dropout gan learning from a dynamic ensemble of discriminators"
    - "dual discriminator generative adversarial nets"

Connected through hyperedge HE-4 (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-5 (induced by source paper "rob gan generator discriminator and adversarial attacker"):
  with members:
    - "cgans with projection discriminator"
    - "stochastic activation pruning for robust adversarial defense"
    - "data augmentation by pairing samples for images classification"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "spectral normalization for generative adversarial networks"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "the gan landscape losses architectures regularization and normalization"):
  with members:
    - "high fidelity image generation with fewer labels"
    - "pros and cons of gan evaluation measures"
    - "spectral normalization for generative adversarial networks"
    - "a large scale study on regularization and normalization in gans"
    - "deep generative models for distribution preserving lossy compression"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "self supervised generative adversarial networks"

Connected through hyperedge HE-7 (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"
    - "generative adversarial parallelization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-8 (induced by source paper "mixture density generative adversarial networks"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "mcgan mean and covariance feature matching gan"
    - "multi agent diverse generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "the numerics of gans"

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)