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: "bilateral adversarial training towards fast training of more robust models against adversarial attacks"
  Abstract: In this paper, we study fast training of adversarially robust models. From the analyses of the state-of-the-art defense method, i.e., the multi-step adversarial training, we hypothesize that the gradient magnitude links to the model robustness. Motivated by this, we propose to perturb both the image and the label during training, which we call Bilateral Adversarial Training (BAT). To generate the adversarial label, we derive an closed-form heuristic solution. To generate the adversarial image, we use one-step targeted attack with the target label being the most confusing class. In the experiment, we first show that random start and the most confusing target attack effectively prevent the label leaking and gradient masking problem. Then coupled with the adversarial label part, our model significantly improves the state-of-the-art results. For example, against PGD100 white-box attack with cross-entropy loss, on CIFAR10, we achieve 63.7\% versus 47.2\%; on SVHN, we achieve 59.1\% versus 42.1\%. At last, the experiment on the very (computationally) challenging ImageNet dataset further demonstrates the effectiveness of our fast method.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fast is better than free revisiting adversarial training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "adversarial robustness against the union of multiple perturbation models"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-3 (induced by source paper "on pruning adversarially robust neural networks"):
  with members:
    - "picking winning tickets before training by preserving gradient flow"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "towards compact and robust deep neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-4 (induced by source paper "defense against adversarial attacks using feature scattering based adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "joint adversarial training incorporating both spatial and pixel attacks"
    - "towards adversarially robust object detection"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"

Connected through hyperedge HE-5 (induced by source paper "one man s trash is another man s treasure resisting adversarial examples by adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "intriguing properties of adversarial training"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "convergence of adversarial training in overparametrized neural networks"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "are labels required for improving adversarial robustness"
    - "defending adversarial attacks by correcting logits"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"

Connected through hyperedge HE-7 (induced by source paper "joint adversarial training incorporating both spatial and pixel attacks"):
  with members:
    - "spatially transformed adversarial examples"
    - "feature denoising for improving adversarial robustness"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "learning and querying fast generative models for reinforcement learning"
    - "deflecting adversarial attacks with pixel deflection"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against 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)