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: "curls whey boosting black box adversarial attacks"
  Abstract: Image classifiers based on deep neural networks suffer from harassment caused by adversarial examples. Two defects exist in black-box iterative attacks that generate adversarial examples by incrementally adjusting the noise-adding direction for each step. On the one hand, existing iterative attacks add noises monotonically along the direction of gradient ascent, resulting in a lack of diversity and adaptability of the generated iterative trajectories. On the other hand, it is trivial to perform adversarial attack by adding excessive noises, but currently there is no refinement mechanism to squeeze redundant noises. In this work, we propose Curls & Whey black-box attack to fix the above two defects. During Curls iteration, by combining gradient ascent and descent, we `curl' up iterative trajectories to integrate more diversity and transferability into adversarial examples. Curls iteration also alleviates the diminishing marginal effect in existing iterative attacks. The Whey optimization further squeezes the `whey' of noises by exploiting the robustness of adversarial perturbation. Extensive experiments on Imagenet and Tiny-Imagenet demonstrate that our approach achieves impressive decrease on noise magnitude in l2 norm. Curls & Whey attack also shows promising transferability against ensemble models as well as adversarially trained models. In addition, we extend our attack to the targeted misclassification, effectively reducing the difficulty of targeted attacks under black-box condition.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "hopskipjumpattack a query efficient decision based attack"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial learning with margin based triplet embedding regularization"

Connected through hyperedge HE-2 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "objects as points"
    - "bottom up object detection by grouping extreme and center points"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "robust adversarial perturbation on deep proposal based models"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-3 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples"):
  with members:
    - "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"
    - "mesonet a compact facial video forgery detection network"

Connected through hyperedge HE-4 (induced by source paper "one man s trash is another man s treasure resisting adversarial examples by adversarial examples"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "metric learning for adversarial robustness"
    - "hopskipjumpattack a query efficient decision based attack"
    - "defending against adversarial attacks by randomized diversification"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a study of black box adversarial attacks in computer vision"):
  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"
    - "adversarial logit pairing"
    - "genattack practical black box attacks with gradient free optimization"
    - "deflecting adversarial attacks with pixel deflection"
    - "black box adversarial attacks with limited queries and information"

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)