human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "adversarial examples improve image recognition"):
  Abstract: Adversarial examples are commonly viewed as a threat to ConvNets. Here we present an opposite perspective: adversarial examples can be used to improve image recognition models if harnessed in the right manner. We propose AdvProp, an enhanced adversarial training scheme which treats adversarial examples as additional examples, to prevent overfitting. Key to our method is the usage of a separate auxiliary batch norm for adversarial examples, as they have different underlying distributions to normal examples. #R##N#We show that AdvProp improves a wide range of models on various image recognition tasks and performs better when the models are bigger. For instance, by applying AdvProp to the latest EfficientNet-B7 [28] on ImageNet, we achieve significant improvements on ImageNet (+0.7%), ImageNet-C (+6.5%), ImageNet-A (+7.0%), Stylized-ImageNet (+4.8%). With an enhanced EfficientNet-B8, our method achieves the state-of-the-art 85.5% ImageNet top-1 accuracy without extra data. This result even surpasses the best model in [20] which is trained with 3.5B Instagram images (~3000X more than ImageNet) and ~9.4X more parameters. Models are available at this https URL.

Includes members:
  Member "improving robustness without sacrificing accuracy with patch gaussian augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "natural adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "randaugment practical automated data augmentation with a reduced search space":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  Member "randaugment practical data augmentation with no separate search":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "theoretically principled trade off between robustness and accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "fast autoaugment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")
  Member "interpreting adversarially trained convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")

Question: Please tell me which class the source paper 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)