human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "ensemble noise simulation to handle uncertainty about gradient based adversarial attacks"):
  with members:
    - "a computationally efficient method for defending adversarial deep learning attacks"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "training multi task adversarial network for extracting noise robust speaker embedding"
    - "breaking transferability of adversarial samples with randomness"
    - "boosting noise robustness of acoustic model via deep adversarial training"
    - "combatting adversarial attacks through denoising and dimensionality reduction a cascaded autoencoder approach"
    - "enhancing robustness of machine learning systems via data transformations"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "can attention masks improve adversarial robustness"):
  with members:
    - "feature denoising for improving adversarial robustness"
    - "towards the first adversarially robust neural network model on mnist"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "segmentation aware convolutional networks using local attention masks"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "rethinking atrous convolution for semantic image segmentation"

Question: Please tell me which class the center node 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)