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: "on the discrimination generalization tradeoff in gans"
  Abstract: Generative adversarial training can be generally understood as minimizing certain moment matching loss defined by a set of discriminator functions, typically neural networks. The discriminator set should be large enough to be able to uniquely identify the true distribution (discriminative), and also be small enough to go beyond memorizing samples (generalizable). In this paper, we show that a discriminator set is guaranteed to be discriminative whenever its linear span is dense in the set of bounded continuous functions. This is a very mild condition satisfied even by neural networks with a single neuron. Further, we develop generalization bounds between the learned distribution and true distribution under different evaluation metrics. When evaluated with neural or Wasserstein distances, our bounds show that generalization is guaranteed as long as the discriminator set is small enough, regardless of the size of the generator or hypothesis set. When evaluated with KL divergence, our bound provides an explanation on the counter-intuitive behaviors of testing likelihood in GAN training. Our analysis sheds lights on understanding the practical performance of GANs.

Connected through hyperedge HE-1 (induced by source paper "when can wasserstein gans minimize wasserstein distance"):
  with members:
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "sgd learns one layer networks in wgans"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "approximability of discriminators implies diversity in gans"
    - "spectral normalization for generative adversarial networks"
    - "negative momentum for improved game dynamics"
    - "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-2 (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans"):
  with members:
    - "naomi non autoregressive multiresolution sequence imputation"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "adversarial audio synthesis"
    - "improved training of wasserstein gans"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"
    - "f divergence inequalities"

Connected through hyperedge HE-3 (induced by source paper "generating high fidelity synthetic time series datasets with doppelganger"):
  with members:
    - "infogan cr disentangling generative adversarial networks with contrastive regularizers"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "a statistical recurrent model on the manifold of symmetric positive definite matrices"
    - "differentially private generative adversarial network"
    - "progressive growing of gans for improved quality stability and variation"
    - "generating multi label discrete patient records using generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "sgd learns one layer networks in wgans"):
  with members:
    - "what is local optimality in nonconvex nonconcave minimax optimization"
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "learning distributions generated by one layer relu networks"
    - "approximability of discriminators implies diversity in gans"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "learning one hidden layer neural networks with landscape design"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "on value discrepancy of imitation learning"):
  with members:
    - "on the correctness and sample complexity of inverse reinforcement learning"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "behavioral cloning from observation"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "end to end learning for self driving cars"
    - "generative adversarial imitation learning"
    - "model free imitation learning with policy optimization"

Connected through hyperedge HE-6 (induced by source paper "generative adversarial nets for robust scatter estimation a proper scoring rule perspective"):
  with members:
    - "deconstructing generative adversarial networks"
    - "approximability of discriminators implies diversity in gans"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "improved techniques for training gans"
    - "least squares generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "on computation and generalization of gans with spectrum control"):
  with members:
    - "cgans with projection discriminator"
    - "learning towards minimum hyperspherical energy"
    - "spectral normalization for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "learning to protect communications with adversarial neural cryptography"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "stabilizing training of generative adversarial networks through regularization"

Connected through hyperedge HE-8 (induced by source paper "approximability of discriminators implies diversity in gans"):
  with members:
    - "pros and cons of gan evaluation measures"
    - "generative adversarial parallelization"
    - "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"
    - "generalization and equilibrium in generative adversarial nets 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)