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: "boosting adversarial attacks with momentum"
  Abstract: Deep neural networks are vulnerable to adversarial examples, which poses security concerns on these algorithms due to the potentially severe consequences. Adversarial attacks serve as an important surrogate to evaluate the robustness of deep learning models before they are deployed. However, most of the existing adversarial attacks can only fool a black-box model with a low success rate because of the coupling of the attack ability and the transferability. To address this issue, we propose a broad class of momentum-based iterative algorithms to boost adversarial attacks. By integrating the momentum term into the iterative process for attacks, our methods can stabilize update directions and escape from poor local maxima during the iterations, resulting in more transferable adversarial examples. To further improve the success rates for black-box attacks, we apply momentum iterative algorithms to an ensemble of models, and show that the adversarially trained models with a strong defense ability are also vulnerable to our black-box attacks. We hope that the proposed methods will serve as a benchmark for evaluating the robustness of various deep models and defense methods. We won the first places in NIPS 2017 Non-targeted Adversarial Attack and Targeted Adversarial Attack competitions.

Connected through hyperedge HE-1 (induced by source paper "adversarial examples opportunities and challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "atmpa attacking machine learning based malware visualization detection methods via adversarial examples"
    - "hcic hardware assisted control flow integrity checking"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "adversarial attacks against medical deep learning systems"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-2 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "tvm end to end optimization stack for deep learning"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "a kernelized manifold mapping to diminish the effect of adversarial perturbations"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "vulnerability analysis of chest x ray image classification against adversarial attacks"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "blind pre processing a robust defense method against adversarial examples"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"

Connected through hyperedge HE-4 (induced by source paper "jumprelu a retrofit defense strategy for adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "trust region based adversarial attack on neural networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-5 (induced by source paper "advhat real world adversarial attack on arcface face id system"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "a general framework for adversarial examples with objectives"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "adversarial patch"
    - "cosface large margin cosine loss for deep face recognition"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "yolov3 an incremental improvement"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-6 (induced by source paper "defective convolutional layers learn robust cnns"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "dropping pixels for adversarial robustness"
    - "adversarial defense by stratified convolutional sparse coding"
    - "dropblock a regularization method for convolutional networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "analyzing the noise robustness of deep neural networks"
    - "sok security and privacy in machine learning"

Connected through hyperedge HE-7 (induced by source paper "once a man towards multi target attack via learning multi target adversarial network once"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "generating adversarial examples with adversarial networks"
    - "sok security and privacy in machine learning"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "attention is all you need"
    - "squeeze and excitation networks"
    - "adversarial transformation networks learning to generate adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "on the effectiveness of low frequency perturbations"):
  with members:
    - "are generative classifiers more robust to adversarial attacks"
    - "adversarial attacks and defences competition"
    - "feature denoising for improving adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "discrete attacks and submodular optimization with applications to text classification"
    - "towards the first adversarially robust neural network model on mnist"
    - "generating natural language adversarial examples"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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)