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 "universal adversarial perturbations to understand robustness of texture vs shape biased training"):
  Abstract: Convolutional Neural Networks (CNNs) used on image classification tasks such as ImageNet have been shown to be biased towards recognizing textures rather than shapes. Recent work has attempted to alleviate this by augmenting the training dataset with shape-based examples to create Stylized-ImageNet. However, in this paper we show that models trained on this dataset remain vulnerable to Universal Adversarial Perturbations (UAPs). We use UAPs to evaluate and compare the robustness of CNN models with varying degrees of shape-based training. We also find that a posteriori fine-tuning on ImageNet negates features learned from training on Stylized-ImageNet. This study reveals an important limitation and reiterates the need for further research into understanding the robustness of CNNs for visual recognition.

Includes members:
  Member "improving the robustness of imagenet classifiers using elements of human visual cognition":
    also connected through other hyperedges:
      (none)
  Member "adversarial training and robustness for multiple perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "procedural noise adversarial examples for black box attacks on deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fundamental tradeoffs between invariance and sensitivity to adversarial perturbations")
  Member "transfer of adversarial robustness between perturbation types":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "approximating cnns with bag of local features models works surprisingly well on imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning representations by predicting bags of visual words")
  Member "sensitivity of deep convolutional networks to gabor noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "testing robustness against unforeseen adversaries")
  Member "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "universal adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")

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)