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: "robustness of classifiers from adversarial to random noise"
  Abstract: Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to random noise. In this paper, we propose to study a \textit{semi-random} noise regime that generalizes both the random and worst-case noise regimes. We propose the first quantitative analysis of the robustness of nonlinear classifiers in this general noise regime. We establish precise theoretical bounds on the robustness of classifiers in this general regime, which depend on the curvature of the classifier's decision boundary. Our bounds confirm and quantify the empirical observations that classifiers satisfying curvature constraints are robust to random noise. Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes. We perform experiments and show that the derived bounds provide very accurate estimates when applied to various state-of-the-art deep neural networks and datasets. This result suggests bounds on the curvature of the classifiers' decision boundaries that we support experimentally, and more generally offers important insights onto the geometry of high dimensional classification problems.

Connected through hyperedge HE-1 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"

Connected through hyperedge HE-2 (induced by source paper "randomization matters how to defend against strong adversarial attacks"):
  with members:
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "robust attacks against multiple classifiers"
    - "adversarial risk via optimal transport and optimal couplings"
    - "on evaluating adversarial robustness"
    - "theoretical evidence for adversarial robustness through randomization"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"

Connected through hyperedge HE-3 (induced by source paper "a fast saddle point dynamical system approach to robust deep learning"):
  with members:
    - "on gradient descent ascent for nonconvex concave minimax problems"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "scaling provable adversarial defenses"
    - "trust region based adversarial attack on neural networks"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust optimization for non convex objectives"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-4 (induced by source paper "geoda a geometric framework for black box adversarial attacks"):
  with members:
    - "a geometry inspired decision based attack"
    - "hopskipjumpattack a query efficient decision based attack"
    - "simple black box adversarial attacks"
    - "stateful detection of black box adversarial attacks"
    - "improving black box adversarial attacks with a transfer based prior"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"

Connected through hyperedge HE-5 (induced by source paper "hold me tight influence of discriminative features on deep network boundaries"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "a fourier perspective on model robustness in computer vision"
    - "pytorch an imperative style high performance deep learning library"
    - "robustness via curvature regularization and vice versa"
    - "adversarial spheres"
    - "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"
    - "on the decision boundary of deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "analyzing accuracy loss in randomized smoothing defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certified adversarial robustness with additive noise"
    - "certified defenses against adversarial examples"
    - "efficient defenses against adversarial attacks"
    - "towards robust neural networks via random self ensemble"
    - "understanding deep learning requires rethinking generalization"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-7 (induced by source paper "adversarial robustness via label smoothing"):
  with members:
    - "adversarial examples opportunities and challenges"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "regularizing neural networks by penalizing confident output distributions"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "on the effectiveness of defensive distillation"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

Connected through hyperedge HE-8 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep 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)