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: "low frequency adversarial perturbation"
  Abstract: Adversarial images aim to change a target model's decision by minimally perturbing a target image. In the black-box setting, the absence of gradient information often renders this search problem costly in terms of query complexity. In this paper we propose to restrict the search for adversarial images to a low frequency domain. This approach is readily compatible with many existing black-box attack frameworks and consistently reduces their query cost by 2 to 4 times. Further, we can circumvent image transformation defenses even when both the model and the defense strategy are unknown. Finally, we demonstrate the efficacy of this technique by fooling the Google Cloud Vision platform with an unprecedented low number of model queries.

Connected through hyperedge HE-1 (induced by source paper "a survey of black box adversarial attacks on computer vision models"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "simple black box adversarial attacks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-2 (induced by source paper "frequency tuned universal adversarial attacks"):
  with members:
    - "a survey of black box adversarial attacks on computer vision models"
    - "on the effectiveness of low frequency perturbations"
    - "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"

Connected through hyperedge HE-3 (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"
    - "on the effectiveness of low frequency perturbations"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a model based derivative free approach to black box adversarial examples bobyqa"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "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"
    - "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 "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"):
  with members:
    - "adversarial attacks and defences competition"
    - "group normalization"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "universal adversarial training"

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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "square attack a query efficient black box adversarial attack via random search"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "sparse and imperceivable adversarial attacks"
    - "black box adversarial attacks with bayesian optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "hopskipjumpattack a query efficient decision based attack"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "there are no bit parts for sign bits in black box attacks"

Connected through hyperedge HE-8 (induced by source paper "simple black box adversarial attacks"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adversarial vulnerability for any classifier"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "are adversarial examples inevitable"
    - "query efficient hard label black box attack an optimization based approach"
    - "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"

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)