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: "generalizable data free objective for crafting universal adversarial perturbations"
  Abstract: Machine learning models are susceptible to adversarial perturbations: small changes to input that can cause large changes in output. It is also demonstrated that there exist input-agnostic perturbations, called universal adversarial perturbations, which can change the inference of target model on most of the data samples. However, existing methods to craft universal perturbations are (i) task specific, (ii) require samples from the training data distribution, and (iii) perform complex optimizations. Also, because of the data dependence, fooling ability of the crafted perturbations is proportional to the available training data. In this paper, we present a novel, generalizable and data-free objective for crafting universal adversarial perturbations. Independent of the underlying task, our objective achieves fooling via corrupting the extracted features at multiple layers. Therefore, the proposed objective is generalizable to craft image-agnostic perturbations across multiple vision tasks such as object recognition, semantic segmentation and depth estimation. In the practical setting of black-box attacking scenario, we show that our objective outperforms the data dependent objectives to fool the learned models. Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations. Significant fooling rates achieved by our objective emphasize that the current deep learning models are now at an increased risk, since our objective generalizes across multiple tasks without the requirement of training data for crafting the perturbations.

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"
    - "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-2 (induced by source paper "data free adversarial perturbations for practical black box attack"):
  with members:
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "universal adversarial perturbations"
    - "adversarial examples in the physical world"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "deepcorrect correcting dnn models against image distortions"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"

Connected through hyperedge HE-4 (induced by source paper "fda feature disruptive attack"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "adversarial logit pairing"
    - "gray box adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-5 (induced by source paper "universal adversarial perturbations for cnn classifiers in eeg based bcis"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "universal adversarial perturbations for speech recognition systems"
    - "active learning for black box adversarial attacks in eeg based brain computer interfaces"
    - "adversarial patch"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "feature selection a data perspective"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial perturbations to understand robustness of texture vs shape biased training"):
  with members:
    - "improving the robustness of imagenet classifiers using elements of human visual cognition"
    - "adversarial training and robustness for multiple perturbations"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "transfer of adversarial robustness between perturbation types"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "sensitivity of deep convolutional networks to gabor noise"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "universal adversarial training"

Connected through hyperedge HE-7 (induced by source paper "enhancing adversarial example transferability with an intermediate level attack"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples attacks and defenses for deep learning"
    - "practical black box attacks against machine learning"
    - "densely connected convolutional networks"
    - "universal adversarial perturbations"

Connected through hyperedge HE-8 (induced by source paper "security of facial forensics models against adversarial attacks"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "forensictransfer weakly supervised domain adaptation for forgery detection"
    - "exposing deep fakes using inconsistent head poses"
    - "exposing deepfake videos by detecting face warping artifacts"
    - "detection of deep network generated images using disparities in color components"
    - "in ictu oculi exposing ai generated fake face videos by detecting eye blinking"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

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)