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 "lipschitz regularized deep neural networks generalize and are adversarially robust"):
  Abstract: In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robustness. The proof of generalization does not overcome the curse of dimensionality, but it is independent of the number of layers in the networks. The adversarial robustness regularization combines adversarial training, which we show to be equivalent to Total Variation regularization, with Lipschitz regularization. We demonstrate empirically that the regularized models are more robust, and that gradient norms of images can be used for attack detection.

Includes members:
  Member "improving the improved training of wasserstein gans a consistency term and its dual effect":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sliced wasserstein generative models")
  Member "sorting out lipschitz function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial robustness through local lipschitzness")
  Member "lipschitz margin training scalable certification of perturbation invariance for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "l2 nonexpansive neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on evaluating adversarial robustness")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "improved robustness to adversarial examples using lipschitz regularization of the loss":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpreting adversarial examples by activation promotion and suppression")
  Member "training for faster adversarial robustness verification via inducing relu stability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lossless compression of deep neural networks")
  Member "a surprising linear relationship predicts test performance in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise")

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)