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: "feature squeezing mitigates and detects carlini wagner adversarial examples"
  Abstract: Feature squeezing is a recently-introduced framework for mitigating and detecting adversarial examples. In previous work, we showed that it is effective against several earlier methods for generating adversarial examples. In this short note, we report on recent results showing that simple feature squeezing techniques also make deep learning models significantly more robust against the Carlini/Wagner attacks, which are the best known adversarial methods discovered to date.

Connected through hyperedge HE-1 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial attacks and defences a survey"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "moving target defense for deep visual sensing against adversarial examples"):
  with members:
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "recovery guarantees for compressible signals with adversarial noise"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "certified adversarial robustness with additive noise"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-4 (induced by source paper "the faults in our pi stars security issues and open challenges in deep reinforcement learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "mitigation of policy manipulation attacks on deep q networks with parameter space noise"
    - "sequential attacks on agents for long term adversarial goals"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "sok security and privacy in machine learning"
    - "a psychopathological approach to safety engineering in ai and agi"
    - "online robust policy learning in the presence of unknown adversaries"
    - "reinforcement learning for autonomous defence in software defined networking"

Connected through hyperedge HE-5 (induced by source paper "clipping free attacks against artificial neural networks"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "on the statistical detection of adversarial examples"
    - "adversarial perturbations against deep neural networks for malware classification"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "end to end learning for self driving cars"
    - "early methods for detecting adversarial images"
    - "adversarial examples detection in deep networks with convolutional filter statistics"

Connected through hyperedge HE-6 (induced by source paper "on lyapunov exponents and adversarial perturbation"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial and clean data are not twins"
    - "safetynet detecting and rejecting adversarial examples robustly"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "on the statistical detection of adversarial examples"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "detecting adversarial attacks on neural network policies with visual foresight"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "decomposing motion and content for natural video sequence prediction"
    - "enhancing robustness of machine learning systems via data transformations"
    - "adversarial and clean data are not twins"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "efficient defenses against adversarial attacks"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "adversarial and clean data are not twins"
    - "analysis of universal adversarial perturbations"
    - "security evaluation of pattern classifiers under attack"
    - "on the statistical detection of adversarial examples"
    - "practical black box attacks against machine learning"
    - "understanding deep learning requires rethinking generalization"

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.CR(Cryptography and Security)