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 "heat and blur an effective and fast defense against adversarial examples"):
  Abstract: The growing incorporation of artificial neural networks (NNs) into many fields, and especially into life-critical systems, is restrained by their vulnerability to adversarial examples (AEs). Some existing defense methods can increase NNs' robustness, but they often require special architecture or training procedures and are irrelevant to already trained models. In this paper, we propose a simple defense that combines feature visualization with input modification, and can, therefore, be applicable to various pre-trained networks. By reviewing several interpretability methods, we gain new insights regarding the influence of AEs on NNs' computation. Based on that, we hypothesize that information about the "true" object is preserved within the NN's activity, even when the input is adversarial, and present a feature visualization version that can extract that information in the form of relevance heatmaps. We then use these heatmaps as a basis for our defense, in which the adversarial effects are corrupted by massive blurring. We also provide a new evaluation metric that can capture the effects of both attacks and defenses more thoroughly and descriptively, and demonstrate the effectiveness of the defense and the utility of the suggested evaluation measurement with VGG19 results on the ImageNet dataset.

Includes members:
  Member "concise explanations of neural networks using adversarial training":
    also connected through other hyperedges:
      (none)
  Member "adversarial examples are a natural consequence of test error in noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "explaining convolutional neural networks using softmax gradient layer wise relevance propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "saliency methods for explaining adversarial attacks":
    also connected through other hyperedges:
      (none)
  Member "interpreting adversarial examples by activation promotion and suppression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "defending against backdoor attack on deep neural networks")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "interpretable deep learning under fire":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "aggregating explanation methods for stable and robust explainability")
  Member "concise and stable explanations using adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ml loo detecting adversarial examples with feature attribution")

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)