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: "robustness via curvature regularization and vice versa"
  Abstract: State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations. One of the most effective strategies to improve robustness is adversarial training. In this paper, we investigate the effect of adversarial training on the geometry of the classification landscape and decision boundaries. We show in particular that adversarial training leads to a significant decrease in the curvature of the loss surface with respect to inputs, leading to a drastically more "linear" behaviour of the network. Using a locally quadratic approximation, we provide theoretical evidence on the existence of a strong relation between large robustness and small curvature. To further show the importance of reduced curvature for improving the robustness, we propose a new regularizer that directly minimizes curvature of the loss surface, and leads to adversarial robustness that is on par with adversarial training. Besides being a more efficient and principled alternative to adversarial training, the proposed regularizer confirms our claims on the importance of exhibiting quasi-linear behavior in the vicinity of data points in order to achieve robustness.

Connected through hyperedge HE-1 (induced by source paper "varmixup exploiting the latent space for robust training and inference"):
  with members:
    - "adversarial domain adaptation with domain mixup"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "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"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"

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 "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "on decision regions of narrow deep neural networks"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-4 (induced by source paper "output diversified initialization for adversarial attacks"):
  with members:
    - "metric learning for adversarial robustness"
    - "certified adversarial robustness via randomized smoothing"
    - "an alternative surrogate loss for pgd based adversarial testing"
    - "theoretically principled trade off between robustness and accuracy"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "feature denoising for improving adversarial robustness"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "hold me tight influence of discriminative features on deep network boundaries"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "a fourier perspective on model robustness in computer vision"
    - "pytorch an imperative style high performance deep learning library"
    - "adversarial spheres"
    - "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"
    - "on the decision boundary of deep neural networks"
    - "with friends like these who needs adversaries"

Connected through hyperedge HE-6 (induced by source paper "deep k nn defense against clean label data poisoning attacks"):
  with members:
    - "transfer learning for hate speech detection in social media"
    - "defending neural backdoors via generative distribution modeling"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "spectral signatures in backdoor attacks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "dual path networks"

Connected through hyperedge HE-7 (induced by source paper "regularization via structural label smoothing"):
  with members:
    - "when does label smoothing help"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning to bound the multi class bayes error"
    - "adversarial logit pairing"
    - "regularizing neural networks by penalizing confident output distributions"
    - "disturblabel regularizing cnn on the loss layer"
    - "meta learning of bounds on the bayes classifier error"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (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"

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)