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: "mitigating adversarial effects through randomization"
  Abstract: Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperceptible perturbations added to clean images can cause convolutional neural networks to fail. In this paper, we propose to utilize randomization at inference time to mitigate adversarial effects. Specifically, we use two randomization operations: random resizing, which resizes the input images to a random size, and random padding, which pads zeros around the input images in a random manner. Extensive experiments demonstrate that the proposed randomization method is very effective at defending against both single-step and iterative attacks. Our method provides the following advantages: 1) no additional training or fine-tuning, 2) very few additional computations, 3) compatible with other adversarial defense methods. By combining the proposed randomization method with an adversarially trained model, it achieves a normalized score of 0.924 (ranked No.2 among 107 defense teams) in the NIPS 2017 adversarial examples defense challenge, which is far better than using adversarial training alone with a normalized score of 0.773 (ranked No.56). The code is public available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "image super resolution as a defense against adversarial attacks"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "ghostimage perception domain attacks against vision based object classification systems"):
  with members:
    - "taxonomy and challenges of out of band signal injection attacks and defenses"
    - "adversarial sensor attack on lidar based perception in autonomous driving"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "advhat real world adversarial attack on arcface face id system"
    - "spatially transformed adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarially trained model compression when robustness meets efficiency"
    - "defensive quantization when efficiency meets robustness"
    - "towards understanding adversarial examples systematically exploring data size task and model factors"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (induced by source paper "on the security of randomized defenses against adversarial samples"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "on evaluating adversarial robustness"
    - "training verified learners with learned verifiers"
    - "breaking transferability of adversarial samples with randomness"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "universal adversarial perturbations for speech recognition systems"

Connected through hyperedge HE-7 (induced by source paper "an empirical evaluation of perturbation based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "defending against whitebox adversarial attacks via randomized discretization"
    - "ppd permutation phase defense against adversarial examples in deep learning"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "the effects of jpeg and jpeg2000 compression on attacks using adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "regularizers for single step adversarial training"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "on evaluating adversarial robustness"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

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)