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: "improving the generalization of adversarial training with domain adaptation"
  Abstract: By injecting adversarial examples into training data, adversarial training is promising for improving the robustness of deep learning models. However, most existing adversarial training approaches are based on a specific type of adversarial attack. It may not provide sufficiently representative samples from the adversarial domain, leading to a weak generalization ability on adversarial examples from other attacks. Moreover, during the adversarial training, adversarial perturbations on inputs are usually crafted by fast single-step adversaries so as to scale to large datasets. This work is mainly focused on the adversarial training yet efficient FGSM adversary. In this scenario, it is difficult to train a model with great generalization due to the lack of representative adversarial samples, aka the samples are unable to accurately reflect the adversarial domain. To alleviate this problem, we propose a novel Adversarial Training with Domain Adaptation (ATDA) method. Our intuition is to regard the adversarial training on FGSM adversary as a domain adaption task with limited number of target domain samples. The main idea is to learn a representation that is semantically meaningful and domain invariant on the clean domain as well as the adversarial domain. Empirical evaluations on Fashion-MNIST, SVHN, CIFAR-10 and CIFAR-100 demonstrate that ATDA can greatly improve the generalization of adversarial training and the smoothness of the learned models, and outperforms state-of-the-art methods on standard benchmark datasets. To show the transfer ability of our method, we also extend ATDA to the adversarial training on iterative attacks such as PGD-Adversial Training (PAT) and the defense performance is improved considerably.

Connected through hyperedge HE-1 (induced by source paper "adversarial distributional training for robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "a direct approach to robust deep learning using adversarial networks"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"

Connected through hyperedge HE-2 (induced by source paper "reliable evaluation of adversarial robustness with an ensemble of diverse parameter free attacks"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "controlling neural level sets"
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "jacobian adversarially regularized networks for robustness"

Connected through hyperedge HE-3 (induced by source paper "robustness for non parametric classification a generic attack and defense"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "robust decision trees against adversarial examples"
    - "on the robustness of deep k nearest neighbors"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust boosted decision stumps and trees against adversarial attacks"
    - "on evaluating adversarial robustness"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"

Connected through hyperedge HE-4 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "at gan a generative attack model for adversarial transferring on generative adversarial nets"
    - "adversarially robust generalization just requires more unlabeled data"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "robust local features for improving the generalization of adversarial training"
    - "adversarial attacks and defences competition"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

Connected through hyperedge HE-5 (induced by source paper "dynamic divide and conquer adversarial training for robust semantic segmentation"):
  with members:
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-6 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "exploring the space of black box attacks on deep neural networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "ape gan adversarial perturbation elimination with gan"
    - "improved training of wasserstein gans"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-7 (induced by source paper "using single step adversarial training to defend iterative adversarial examples"):
  with members:
    - "zk gandef a gan based zero knowledge adversarial training defense for neural networks"
    - "adversarial training for free"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "magnet a two pronged defense against adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-8 (induced by source paper "adversarial robustness vs model compression or both"):
  with members:
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "a direct approach to robust deep learning using adversarial networks"
    - "defensive quantization when efficiency meets robustness"
    - "interpreting adversarial examples by activation promotion and suppression"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "sparse dnns with improved adversarial robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

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.LG(Machine Learning)