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: "adversarial logit pairing"
  Abstract: In this paper, we develop improved techniques for defending against adversarial examples at scale. First, we implement the state of the art version of adversarial training at unprecedented scale on ImageNet and investigate whether it remains effective in this setting - an important open scientific question (Athalye et al., 2018). Next, we introduce enhanced defenses using a technique we call logit pairing, a method that encourages logits for pairs of examples to be similar. When applied to clean examples and their adversarial counterparts, logit pairing improves accuracy on adversarial examples over vanilla adversarial training; we also find that logit pairing on clean examples only is competitive with adversarial training in terms of accuracy on two datasets. Finally, we show that adversarial logit pairing achieves the state of the art defense on ImageNet against PGD white box attacks, with an accuracy improvement from 1.5% to 27.9%. Adversarial logit pairing also successfully damages the current state of the art defense against black box attacks on ImageNet (Tramer et al., 2018), dropping its accuracy from 66.6% to 47.1%. With this new accuracy drop, adversarial logit pairing ties with Tramer et al.(2018) for the state of the art on black box attacks on ImageNet.

Connected through hyperedge HE-1 (induced by source paper "fast is better than free revisiting adversarial training"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "me net towards effective adversarial robustness with matrix estimation"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "adversarial robustness against the union of multiple perturbation models"
    - "on evaluating adversarial robustness"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-2 (induced by source paper "adversarial distributional training for robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "a direct approach to robust deep learning using adversarial networks"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods"):
  with members:
    - "cutmix regularization strategy to train strong classifiers with localizable features"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "dropblock a regularization method for convolutional networks"
    - "feature denoising for improving adversarial robustness"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "autoaugment learning augmentation policies from data"
    - "shakedrop regularization for deep residual learning"
    - "generalisation in humans and deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial vertex mixup toward better adversarially robust generalization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mixup inference better exploiting mixup to defend adversarial attacks"
    - "simple black box adversarial attacks"
    - "theoretically principled trade off between robustness and accuracy"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"

Connected through hyperedge HE-5 (induced by source paper "regularizers for single step adversarial training"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "on evaluating adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "certified defenses against adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-6 (induced by source paper "exploiting verified neural networks via floating point numerical error"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "correctness verification of neural networks"
    - "training verified learners with learned verifiers"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "efficient neural network robustness certification with general activation functions"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "evaluating robustness of neural networks with mixed integer programming"

Connected through hyperedge HE-7 (induced by source paper "macer attack free and scalable robust training via maximizing certified radius"):
  with members:
    - "mma training direct input space margin maximization through adversarial training"
    - "certified adversarial robustness via randomized smoothing"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-8 (induced by source paper "dynamic divide and conquer adversarial training for robust semantic segmentation"):
  with members:
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "feature denoising for improving adversarial robustness"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "improving the generalization of adversarial training with domain adaptation"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

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)