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 "transferable adversarial robustness using adversarially trained autoencoders"):
  Abstract: Adversarial machine learning is a well-studied field of research where an adversary causes predictable errors in a machine learning algorithm through careful manipulation of the input. Numerous techniques have been proposed to harden machine learning algorithms and mitigate the effect of adversarial attacks. Of these techniques, adversarial training, which augments the training data with adversarial samples, has proven to be an effective defensive technique with respect to a certain class of attacks. However, adversarial training is computationally expensive and its improvements are limited to a single classifier. In this paper, we propose Adversarially-Trained Autoencoder Augmentation, the first transferable adversarial defense that is robust to certain adaptive adversaries. We disentangle adversarial robustness from the classification pipeline by adversarially training an autoencoder with respect to the classification loss of a naturally trained classifier. The main advantage of our work is that the autoencoders can be reused to protect other vulnerable classifiers without additional training. We show that our approach improves the adversarial robustness of a naturally trained classifier by at least 45\% in MNIST and Fashion-MNIST datasets despite no additional training. On CIFAR-10, we can train a single autoencoder to protect multiple naturally trained classifiers and achieve adversarial performance on par or better than adversarial training. Finally, using a natural image corruption dataset, we show that our approach improves robustness to naturally corrupted images, which has been identified as strongly indicative of true adversarial robustness.

Includes members:
  Member "benchmarking neural network robustness to common corruptions and perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "adversarial training for free":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "feature denoising for improving adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "deep k nearest neighbors towards confident interpretable and robust deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "adversarial logit pairing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast is better than free revisiting adversarial training")
  Member "towards the first adversarially robust neural network model on mnist":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nass optimizing secure inference via neural architecture search")

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)