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 dnn robustness to adversarial attacks using jacobian regularization"
  Abstract: Deep neural networks have lately shown tremendous performance in various applications including vision and speech processing tasks. However, alongside their ability to perform these tasks with such high accuracy, it has been shown that they are highly susceptible to adversarial attacks: a small change of the input would cause the network to err with high confidence. This phenomenon exposes an inherent fault in these networks and their ability to generalize well. For this reason, providing robustness to adversarial attacks is an important challenge in networks training, which has led to an extensive research. In this work, we suggest a theoretically inspired novel approach to improve the networks' robustness. Our method applies regularization using the Frobenius norm of the Jacobian of the network, which is applied as post-processing, after regular training has finished. We demonstrate empirically that it leads to enhanced robustness results with a minimal change in the original network's accuracy.

Connected through hyperedge HE-1 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based dropout layer for weakly supervised object localization"
    - "robust attribution regularization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "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"
    - "harnessing the vulnerability of latent layers in adversarially trained models"

Connected through hyperedge HE-2 (induced by source paper "procedural noise adversarial examples for black box attacks on deep convolutional networks"):
  with members:
    - "on evaluating adversarial robustness"
    - "spatially transformed adversarial examples"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generating adversarial examples with adversarial networks"
    - "sok security and privacy in machine learning"
    - "defense against the dark arts an overview of adversarial example security research and future research directions"
    - "audio adversarial examples targeted attacks on speech to text"

Connected through hyperedge HE-3 (induced by source paper "adversarial training for free"):
  with members:
    - "label smoothing and logit squeezing a replacement for adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "stochastic activation pruning for robust adversarial defense"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-4 (induced by source paper "a useful taxonomy for adversarial robustness of neural networks"):
  with members:
    - "adversarial defense by suppressing high frequency components"
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "metric learning for adversarial robustness"
    - "adversarial robustness may be at odds with simplicity"
    - "are labels required for improving adversarial robustness"
    - "batch normalization is a cause of adversarial vulnerability"
    - "towards understanding adversarial examples systematically exploring data size task and model factors"
    - "adversarial attacks and defenses in images graphs and text a review"

Connected through hyperedge HE-5 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "defending against adversarial examples with k nearest neighbor"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "dnn or k nn that is the generalize vs memorize question"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-6 (induced by source paper "adversarial defense by restricting the hidden space of deep neural networks"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial attacks and defences competition"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial logit pairing"
    - "improving the generalization of adversarial training with domain adaptation"
    - "interpreting adversarial robustness a view from decision surface in input space"

Connected through hyperedge HE-7 (induced by source paper "does interpretability of neural networks imply adversarial robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial robustness may be at odds with simplicity"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "sorting out lipschitz function approximation"
    - "adversarial spheres"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-8 (induced by source paper "jacobian adversarially regularized networks for robustness"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial examples are a natural consequence of test error in noise"
    - "diagnosing and enhancing vae models"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "on evaluating adversarial robustness"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"

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)