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: "simple physical adversarial examples against end to end autonomous driving models"
  Abstract: Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images, end-to-end demonstrations of successful attacks, which manipulate the physical environment and result in physical consequences, are scarce. Moreover, attacks typically involve carefully constructed adversarial examples at the level of pixels. We demonstrate the first end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are surprisingly easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective, and others that are less vulnerable (e.g., driving straight). Further, we use network deconvolution to demonstrate that the attacks succeed by inducing activation patterns similar to entirely different scenarios used in training.

Connected through hyperedge HE-1 (induced by source paper "trust but verify an information theoretic explanation for the adversarial fragility of machine learning systems and a general defense against adversarial attacks"):
  with members:
    - "discretization based solutions for secure machine learning against adversarial attacks"
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "weight map layer for noise and adversarial attack robustness"
    - "efficient decision based black box adversarial attacks on face recognition"
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "simple black box adversarial attacks"

Connected through hyperedge HE-2 (induced by source paper "moving target defense for deep visual sensing against adversarial examples"):
  with members:
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "detecting and correcting adversarial images using image processing operations"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "protecting jpeg images against adversarial attacks"
    - "adef an iterative algorithm to construct adversarial deformations"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial and clean data are not twins"

Connected through hyperedge HE-4 (induced by source paper "defending against physically realizable attacks on image classification"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "scaling provable adversarial defenses"
    - "sentinet detecting physical attacks against deep learning systems"
    - "curriculum adversarial training"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient formal safety analysis of neural networks"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "attacking vision based perception in end to end autonomous driving models"):
  with members:
    - "key ingredients of self driving cars"
    - "requirements driven test generation for autonomous vehicles with machine learning components"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"
    - "semantic adversarial deep learning"
    - "baidu apollo em motion planner"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-6 (induced by source paper "dopa a comprehensive cnn detection methodology against physical adversarial attacks"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "a multiversion programming inspired approach to detecting audio adversarial examples"
    - "local gradients smoothing defense against localized adversarial attacks"
    - "characterizing audio adversarial examples using temporal dependency"
    - "robust physical world attacks on deep learning models"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-7 (induced by source paper "detecting and correcting adversarial images using image processing operations and convolutional neural networks"):
  with members:
    - "adversarial attacks and defenses in images graphs and text a review"
    - "adef an iterative algorithm to construct adversarial deformations"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial and clean data are not twins"
    - "towards robust detection of adversarial examples"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "on the statistical detection of adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "deception through half truths"):
  with members:
    - "deception by design evidence based signaling games for network defense"
    - "algorithmic bayesian persuasion"

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.RO(Robotics)