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 "on lipschitz bounds of general convolutional neural networks"):
  Abstract: Many convolutional neural networks (CNNs) have a feed-forward structure. In this paper, a linear program that estimates the Lipschitz bound of such CNNs is proposed. Several CNNs, including the scattering networks, the AlexNet and the GoogleNet, are studied numerically and compared to the theoretical bounds. Next, concentration inequalities of the output distribution to a stationary random input signal expressed in terms of the Lipschitz bound are established. The Lipschitz bound is further used to establish a nonlinear discriminant analysis designed to measure the separation between features of different classes.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "batch normalization biases deep residual networks towards shallow paths")
  Member "lipschitz regularized deep neural networks generalize and are adversarially robust":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial robustness through local lipschitzness")
  Member "lipschitz properties for deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient and accurate estimation of lipschitz constants for deep neural networks")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "universal adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial ranking attack and defense")
  Member "distillation as a defense to adversarial perturbations against deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "deep convolutional neural networks based on semi discrete frames":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gabor frames and deep scattering networks in audio processing")

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.IT(Information Theory)