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: "regularizing deep networks using efficient layerwise adversarial training"
  Abstract: Adversarial training has been shown to regularize deep neural networks in addition to increasing their robustness to adversarial examples. However, its impact on very deep state of the art networks has not been fully investigated. In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training. We use these perturbations to train very deep models such as ResNets and show improvement in performance both on adversarial and original test data. Our experiments highlight the benefits of perturbing intermediate layer activations compared to perturbing only the inputs. The results on CIFAR-10 and CIFAR-100 datasets show the merits of the proposed adversarial training approach. Additional results on WideResNets show that our approach provides significant improvement in classification accuracy for a given base model, outperforming dropout and other base models of larger size.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "image super resolution as a defense against adversarial attacks"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "concise explanations of neural networks using adversarial training"):
  with members:
    - "bridging adversarial robustness and gradient interpretability"
    - "on the robustness of interpretability methods"
    - "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-3 (induced by source paper "dla dense layer analysis for adversarial example detection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial attacks and defences a survey"
    - "audio adversarial examples targeted attacks on speech to text"
    - "testing deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "improving neural language modeling via adversarial training"):
  with members:
    - "partially shuffling the training data to improve language models"
    - "representation degeneration problem in training natural language generation models"
    - "fast parametric learning with activation memorization"
    - "learning towards minimum hyperspherical energy"
    - "frage frequency agnostic word representation"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"

Connected through hyperedge HE-5 (induced by source paper "rnn test adversarial testing framework for recurrent neural network systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deep text classification can be fooled"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "audio adversarial examples targeted attacks on speech to text"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "combinatorial testing for deep learning systems"

Connected through hyperedge HE-6 (induced by source paper "decaying momentum helps neural network training"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "momentum based variance reduction in non convex sgd"
    - "on the convergence of adam and beyond"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "aggregated momentum stability through passive damping"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "negative momentum for improved game dynamics"

Connected through hyperedge HE-7 (induced by source paper "harnessing the vulnerability of latent layers in adversarially trained models"):
  with members:
    - "spatially transformed adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "feature denoising for improving adversarial robustness"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "on the robustness of interpretability methods"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "training robust deep neural networks via adversarial noise propagation"):
  with members:
    - "image synthesis with a single robust classifier"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "on evaluating adversarial robustness"
    - "stochastic activation pruning for robust adversarial defense"
    - "deep defense training dnns with improved adversarial robustness"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"

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)