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: "a simple explanation for the existence of adversarial examples with small hamming distance"
  Abstract: The existence of adversarial examples in which an imperceptible change in the input can fool well trained neural networks was experimentally discovered by Szegedy et al in 2013, who called them "Intriguing properties of neural networks". Since then, this topic had become one of the hottest research areas within machine learning, but the ease with which we can switch between any two decisions in targeted attacks is still far from being understood, and in particular it is not clear which parameters determine the number of input coordinates we have to change in order to mislead the network. In this paper we develop a simple mathematical framework which enables us to think about this baffling phenomenon from a fresh perspective, turning it into a natural consequence of the geometry of $\mathbb{R}^n$ with the $L_0$ (Hamming) metric, which can be quantitatively analyzed. In particular, we explain why we should expect to find targeted adversarial examples with Hamming distance of roughly $m$ in arbitrarily deep neural networks which are designed to distinguish between $m$ input classes.

Connected through hyperedge HE-1 (induced by source paper "on the security of randomized defenses against adversarial samples"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "on evaluating adversarial robustness"
    - "training verified learners with learned verifiers"
    - "breaking transferability of adversarial samples with randomness"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-2 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples"):
  with members:
    - "improving the generalization of adversarial training with domain adaptation"
    - "exploring the space of black box attacks on deep neural networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "ape gan adversarial perturbation elimination with gan"
    - "improved training of wasserstein gans"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-3 (induced by source paper "sniff reverse engineering of neural networks with fault attacks"):
  with members:
    - "high fidelity extraction of neural network models"
    - "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"
    - "stealing hyperparameters in machine learning"
    - "learning transferable architectures for scalable image recognition"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-4 (induced by source paper "towards identifying and closing gaps in assurance of autonomous road vehicles a collection of technical notes part 1"):
  with members:
    - "reachability analysis of deep neural networks with provable guarantees"
    - "sok security and privacy in machine learning"
    - "toward scalable verification for safety critical deep networks"
    - "motivating the rules of the game for adversarial example research"
    - "developing bug free machine learning systems with formal mathematics"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "safety verification of deep neural networks"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-5 (induced by source paper "try depth instead of weight correlations mean field is a less restrictive assumption for deeper networks"):
  with members:
    - "functional variational bayesian neural networks"
    - "on the convergence of adam and beyond"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "neural variational inference and learning in belief networks"
    - "nonparametric variational inference"

Connected through hyperedge HE-6 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "batch normalization is a cause of adversarial vulnerability"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "learning robust representations by projecting superficial statistics out"
    - "the curse of concentration in robust learning evasion and poisoning attacks from concentration of measure"
    - "sparse dnns with improved adversarial robustness"

Connected through hyperedge HE-7 (induced by source paper "a general framework for adversarial examples with objectives"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "counterstrike defending deep learning architectures against adversarial samples by langevin dynamics with supervised denoising autoencoder"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "generating adversarial examples with adversarial networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "motivating the rules of the game for adversarial example research"

Connected through hyperedge HE-8 (induced by source paper "when explainability meets adversarial learning detecting adversarial examples using shap signatures"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "on evaluating adversarial robustness"
    - "visual interpretability for deep learning a survey"
    - "detecting adversarial perturbations through spatial behavior in activation spaces"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "towards robust detection of adversarial examples"

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)