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: "robust local features for improving the generalization of adversarial training"
  Abstract: Adversarial training has been demonstrated as one of the most effective methods for training robust models so as to defend against adversarial examples. However, adversarial training often lacks adversarially robust generalization on unseen data. Recent works show that adversarially trained models may be more biased towards global structure features. Instead, in this work, we would like to investigate the relationship between the generalization of adversarial training and the robust local features, as the local features generalize well for unseen shape variation. To learn the robust local features, we develop a Random Block Shuffle (RBS) transformation to break up the global structure features on normal adversarial examples. We continue to propose a new approach called Robust Local Features for Adversarial Training (RLFAT), which first learns the robust local features by adversarial training on the RBS-transformed adversarial examples, and then transfers the robust local features into the training of normal adversarial examples. Finally, we implement RLFAT in two currently state-of-the-art adversarial training frameworks. Extensive experiments on STL-10, CIFAR-10, CIFAR-100 datasets show that RLFAT improves the adversarially robust generalization as well as the standard generalization of adversarial training. Additionally, we demonstrate that our method captures more local features of the object, aligning better with human perception.

Connected through hyperedge HE-1 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "adversarially robust generalization just requires more unlabeled data"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "adversarial attacks and defences competition"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "improving the generalization of adversarial training with domain adaptation"
    - "defense against adversarial attacks using high level representation guided denoiser"

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)