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: "random smoothing might be unable to certify ell_ infty robustness for high dimensional images"
  Abstract: We show a hardness result for random smoothing to achieve certified adversarial robustness against attacks in the $\ell_p$ ball of radius $\epsilon$ when $p>2$. Although random smoothing has been well understood for the $\ell_2$ case using the Gaussian distribution, much remains unknown concerning the existence of a noise distribution that works for the case of $p>2$. This has been posed as an open problem by Cohen et al. (2019) and includes many significant paradigms such as the $\ell_\infty$ threat model. In this work, we show that under certain regularity conditions, any noise distribution $\mathcal{D}$ over $\mathbb{R}^d$ that provides $\ell_p$ robustness with $p>2$ must satisfy $\mathbb{E}\eta_i^2=\Omega(d^{1-2/p}\epsilon^2/\delta^2)$ for 99% of the features (pixels) of vector $\eta$ drawn from $\mathcal{D}$, where $\epsilon$ is the robust radius and $\delta$ measures the score gap between the highest score and the runner-up. Therefore, for high-dimensional images with pixel values bounded in $[0,255]$, the required noise will eventually dominate the useful information in the images, leading to trivial smoothed classifiers.

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"
    - "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"
    - "theoretical evidence for adversarial robustness through randomization the case of the exponential family"

Connected through hyperedge HE-2 (induced by source paper "de randomized smoothing for certifiable defense against patch attacks"):
  with members:
    - "curse of dimensionality on randomized smoothing for certifiable robustness"
    - "randomized smoothing of all shapes and sizes"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "certified defenses for adversarial patches"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "robustness certificates for sparse adversarial attacks by randomized ablation"
    - "certified adversarial robustness via randomized smoothing"
    - "certifiably robust interpretation in deep learning"

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)