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 "unifying bilateral filtering and adversarial training for robust neural networks"):
  Abstract: Recent analysis of deep neural networks has revealed their vulnerability to carefully structured adversarial examples. Many effective algorithms exist to craft these adversarial examples, but performant defenses seem to be far away. In this work, we attempt to combine denoising and robust optimization methods into a unified defense which we found to not only work extremely well, but also makes our model robust against future adversarial attacks. We explore the use of bilateral filtering as a projection back to the space of natural images. We first show that with carefully chosen parameters, bilateral filtering can remove more than 90% of adversarial examples from a variety of different attacks. We then adapt our recovery method as a trainable layer in a neural network. When trained under the adversarial training framework, we show that the resulting model is hard to fool with even the best attack methods.

Includes members:
  Member "feature squeezing detecting adversarial examples in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "robust physical world attacks on deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "magnet a two pronged defense against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "defense against adversarial attacks using high level representation guided denoiser":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "technical report on the cleverhans v2 1 0 adversarial examples library":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "adversarial examples detection in deep networks with convolutional filter statistics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "raid randomized adversarial input detection for neural networks")

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)