human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation"):
  Abstract: Generative Adversarial Networks (GANs) have achieved great success in unsupervised learning. Despite the remarkable empirical performance, there are limited theoretical understandings on the statistical properties of GANs. This paper provides statistical guarantees of GANs for the estimation of data distributions which have densities in a Holder space. Our main result shows that, if the generator and discriminator network architectures are properly chosen (universally for all distributions with Holder densities), GANs are consistent estimators of the data distributions under strong discrepancy metrics, such as the Wasserstein distance. To our best knowledge, this is the first statistical theory of GANs for Holder densities. In comparison with existing works, our theory requires minimum assumptions on data distributions. Our generator and discriminator networks utilize general weight matrices and the non-invertible ReLU activation function, while many existing works only apply to invertible weight matrices and invertible activation functions. In our analysis, we decompose the error into a statistical error and an approximation error by a new oracle inequality, which may be of independent interest.

Includes members:
  Member "width provably matters in optimization for deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "improving generalization and stability of generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a novel framework for selection of gans for an application")
  Member "efficient approximation of deep relu networks for functions on low dimensional manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nonparametric regression on low dimensional manifolds using deep relu networks")
  Member "fine grained analysis of optimization and generalization for overparameterized two layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "learning and generalization in overparameterized neural networks going beyond two layers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "gradient descent finds global minima of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "improving gan training via binarized representation entropy bre regularization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "content and colour distillation for learning image translations with the spatial profile loss")

Question: Please tell me which class the source paper 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)