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: "on the effectiveness of low frequency perturbations"
  Abstract: Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains. In addition, recent work has shown that constraining the attack space to a low frequency regime is particularly effective. Yet, it remains unclear whether this is due to generally constraining the attack search space or specifically removing high frequency components from consideration. By systematically controlling the frequency components of the perturbation, evaluating against the top-placing defense submissions in the NeurIPS 2017 competition, we empirically show that performance improvements in both the white-box and black-box transfer settings are yielded only when low frequency components are preserved. In fact, the defended models based on adversarial training are roughly as vulnerable to low frequency perturbations as undefended models, suggesting that the purported robustness of state-of-the-art ImageNet defenses is reliant upon adversarial perturbations being high frequency in nature. We do find that under $\ell_\infty$ $\epsilon=16/255$, the competition distortion bound, low frequency perturbations are indeed perceptible. This questions the use of the $\ell_\infty$-norm, in particular, as a distortion metric, and, in turn, suggests that explicitly considering the frequency space is promising for learning robust models which better align with human perception.

Connected through hyperedge HE-1 (induced by source paper "frequency tuned universal adversarial attacks"):
  with members:
    - "a survey of black box adversarial attacks on computer vision models"
    - "a geometry inspired decision based attack"
    - "defending against universal attacks through selective feature regeneration"
    - "simple black box adversarial attacks"
    - "a study of black box adversarial attacks in computer vision"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "high frequency component helps explain the generalization of convolutional neural networks"
    - "universal adversarial training"

Connected through hyperedge HE-2 (induced by source paper "mma training direct input space margin maximization through adversarial training"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified adversarial robustness via randomized smoothing"
    - "are labels required for improving adversarial robustness"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "using pre training can improve model robustness and uncertainty"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "hold me tight influence of discriminative features on deep network boundaries"):
  with members:
    - "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"
    - "with friends like these who needs adversaries"

Connected through hyperedge HE-4 (induced by source paper "a model based derivative free approach to black box adversarial examples bobyqa"):
  with members:
    - "fake news detection on social media using geometric deep learning"
    - "square attack a query efficient black box adversarial attack via random search"
    - "stochastic activation pruning for robust adversarial defense"
    - "darts deceiving autonomous cars with toxic signs"
    - "low frequency adversarial perturbation"
    - "robust physical world attacks on deep learning models"
    - "practical black box attacks against machine learning"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-5 (induced by source paper "high frequency component helps explain the generalization of convolutional neural networks"):
  with members:
    - "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"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "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-6 (induced by source paper "mitigating large adversarial perturbations on x mas x minus moving averaged samples"):
  with members:
    - "deflecting adversarial attacks with pixel deflection"
    - "low frequency adversarial perturbation"
    - "countering adversarial images using input transformations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "a study of the effect of jpg compression on adversarial images"
    - "adversarial machine learning at scale"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "low frequency adversarial perturbation"):
  with members:
    - "simple black box adversarial attacks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on machine learning models"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"

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.CV(Computer Vision and Pattern Recognition)