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: "certified adversarial robustness with additive noise"
  Abstract: The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defensive models has been considered, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that the proposed method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space"):
  with members:
    - "jumprelu a retrofit defense strategy for adversarial attacks"
    - "an empirical investigation of randomized defenses against adversarial attacks"
    - "robust neural networks using randomized adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "deep learning for single image super resolution a brief review"
    - "deep learning for multiple image super resolution"
    - "sampling and super resolution of sparse signals beyond the fourier domain"
    - "deep learning for image super resolution a survey"

Connected through hyperedge HE-2 (induced by source paper "black box smoothing a provable defense for pretrained classifiers"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "simple black box adversarial attacks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "adversarial risk and the dangers of evaluating against weak attacks"

Connected through hyperedge HE-3 (induced by source paper "on pruning adversarially robust neural networks"):
  with members:
    - "picking winning tickets before training by preserving gradient flow"
    - "certified adversarial robustness via randomized smoothing"
    - "intriguing properties of adversarial training"
    - "robust sparse regularization simultaneously optimizing neural network robustness and compactness"
    - "towards compact and robust deep neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing"):
  with members:
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "on defending against label flipping attacks on malware detection systems"
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "efficient algorithms and lower bounds for robust linear regression"
    - "representer point selection for explaining deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles"):
  with members:
    - "enhancing cross task transferability of adversarial examples with dispersion reduction"
    - "autonomous human activity classification from ego vision camera and accelerometer data"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "scaling provable adversarial defenses"
    - "stochastic activation pruning for robust adversarial defense"
    - "autonomously and simultaneously refining deep neural network parameters by a bi generative adversarial network aided genetic algorithm"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-7 (induced by source paper "bayes probe distribution guided sampling for prediction level sets"):
  with members:
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "out of distribution detection in classifiers via generation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "pyro deep universal probabilistic programming"
    - "this looks like that deep learning for interpretable image recognition"
    - "sim to real learning agile locomotion for quadruped robots"
    - "sanity checks for saliency maps"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "randomization matters how to defend against strong adversarial attacks"):
  with members:
    - "on the hardness of robust classification"
    - "certified adversarial robustness via randomized smoothing"
    - "lower bounds on adversarial robustness from optimal transport"
    - "robust attacks against multiple classifiers"
    - "adversarial risk via optimal transport and optimal couplings"
    - "on evaluating adversarial robustness"
    - "theoretical evidence for adversarial robustness through randomization"
    - "resnets ensemble via the feynman kac formalism to improve natural and robust accuracies"

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)