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: "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.

Connected through hyperedge HE-1 (induced by source paper "adversarial robustness through local lipschitzness"):
  with members:
    - "randomized smoothing of all shapes and sizes"
    - "analyzing accuracy loss in randomized smoothing defenses"
    - "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
    - "rethinking randomized smoothing for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training can hurt generalization"

Connected through hyperedge HE-2 (induced by source paper "plug and play methods provably converge with properly trained denoisers"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "denoising prior driven deep neural network for image restoration"
    - "regularization by denoising clarifications and new interpretations"
    - "regularized fourier ptychography using an online plug and play algorithm"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "neural nearest neighbors networks"

Connected through hyperedge HE-3 (induced by source paper "universal lipschitz approximation in bounded depth neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "scaling provable adversarial defenses"
    - "sorting out lipschitz function approximation"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-4 (induced by source paper "partial differential equation regularization for supervised machine learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "improved regularization of convolutional neural networks with cutout"
    - "towards robust neural networks via random self ensemble"
    - "understanding deep learning requires rethinking generalization"
    - "towards evaluating the robustness of neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "train faster generalize better stability of stochastic gradient descent"
    - "robustness and generalization"

Connected through hyperedge HE-5 (induced by source paper "the coupling effect of lipschitz regularization in deep neural networks"):
  with members:
    - "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "improved robustness to adversarial examples using lipschitz regularization of the loss"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "the power of depth for feedforward neural networks"
    - "adam a method for stochastic optimization"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-6 (induced by source paper "on lipschitz bounds of general convolutional neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "lipschitz properties for deep convolutional networks"
    - "improved training of wasserstein gans"
    - "universal adversarial perturbations"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "deep convolutional neural networks based on semi discrete frames"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

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)