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: "limitations of the lipschitz constant as a defense against adversarial examples"
  Abstract: Several recent papers have discussed utilizing Lipschitz constants to limit the susceptibility of neural networks to adversarial examples. We analyze recently proposed methods for computing the Lipschitz constant. We show that the Lipschitz constant may indeed enable adversarially robust neural networks. However, the methods currently employed for computing it suffer from theoretical and practical limitations. We argue that addressing this shortcoming is a promising direction for future research into certified adversarial defenses.

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 "preventing gradient attenuation in lipschitz constrained convolutional networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "provable robustness against all adversarial l_p perturbations for p geq 1"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"
    - "sorting out lipschitz function approximation"
    - "the singular values of convolutional layers"

Connected through hyperedge HE-3 (induced by source paper "defending against adversarial examples with k nearest neighbor"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial training with voronoi constraints"
    - "sorting out lipschitz function approximation"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "l2 nonexpansive neural networks"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-4 (induced by source paper "efficient and accurate estimation of lipschitz constants for deep neural networks"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "scaling provable adversarial defenses"
    - "on lipschitz bounds of general convolutional neural networks"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "reachability analysis of deep neural networks with provable guarantees"

Connected through hyperedge HE-5 (induced by source paper "universal lipschitz approximation in bounded depth neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "scaling provable adversarial defenses"
    - "lipschitz regularized deep neural networks generalize and are adversarially robust"
    - "sorting out lipschitz function approximation"
    - "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-6 (induced by source paper "global adversarial attacks for assessing deep learning robustness"):
  with members:
    - "generating realistic unrestricted adversarial inputs using dual objective gan training"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "gradient masking causes clever to overestimate adversarial perturbation size"
    - "analysis of universal adversarial perturbations"
    - "deep learning in the automotive industry applications and tools"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-7 (induced by source paper "sorting out lipschitz function approximation"):
  with members:
    - "the singular values of convolutional layers"
    - "aggregated momentum stability through passive damping"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "exploring generalization in deep learning"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

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)