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: "bridging adversarial robustness and gradient interpretability"
  Abstract: Adversarial training is a training scheme designed to counter adversarial attacks by augmenting the training dataset with adversarial examples. Surprisingly, several studies have observed that loss gradients from adversarially trained DNNs are visually more interpretable than those from standard DNNs. Although this phenomenon is interesting, there are only few works that have offered an explanation. In this paper, we attempted to bridge this gap between adversarial robustness and gradient interpretability. To this end, we identified that loss gradients from adversarially trained DNNs align better with human perception because adversarial training restricts gradients closer to the image manifold. We then demonstrated that adversarial training causes loss gradients to be quantitatively meaningful. Finally, we showed that under the adversarial training framework, there exists an empirical trade-off between test accuracy and loss gradient interpretability and proposed two potential approaches to resolving this trade-off.

Connected through hyperedge HE-1 (induced by source paper "concise explanations of neural networks using adversarial training"):
  with members:
    - "on the robustness of interpretability methods"
    - "regularizing deep networks using efficient layerwise adversarial training"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "the limitations of deep learning in adversarial settings"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-2 (induced by source paper "adversarial neural pruning with latent vulnerability suppression"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "using pre training can improve model robustness and uncertainty"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "sparse dnns with improved adversarial robustness"
    - "a spectral view of adversarially robust features"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards the first adversarially robust neural network model on mnist"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"

Connected through hyperedge HE-3 (induced by source paper "adversarial symmetric gans bridging adversarial samples and adversarial networks"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "generating adversarial examples with adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "ape gan adversarial perturbation elimination with gan"
    - "wasserstein divergence for gans"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "why are saliency maps noisy cause of and solution to noisy saliency maps"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "theoretically principled trade off between robustness and accuracy"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "evaluating feature importance estimates"
    - "smoothgrad removing noise by adding noise"
    - "gradients of counterfactuals"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep 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)