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: "logit pairing methods can fool gradient based attacks"
  Abstract: Recently, Kannan et al. [2018] proposed several logit regularization methods to improve the adversarial robustness of classifiers. We show that the computationally fast methods they propose - Clean Logit Pairing (CLP) and Logit Squeezing (LSQ) - just make the gradient-based optimization problem of crafting adversarial examples harder without providing actual robustness. We find that Adversarial Logit Pairing (ALP) may indeed provide robustness against adversarial examples, especially when combined with adversarial training, and we examine it in a variety of settings. However, the increase in adversarial accuracy is much smaller than previously claimed. Finally, our results suggest that the evaluation against an iterative PGD attack relies heavily on the parameters used and may result in false conclusions regarding robustness of a model.

Connected through hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "adversarial training and robustness for multiple perturbations"
    - "certified adversarial robustness via randomized smoothing"
    - "deep double descent where bigger models and more data hurt"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "me net towards effective adversarial robustness with matrix estimation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "on evaluating adversarial robustness"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "the efficacy of shield under different threat models"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "generative adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-4 (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"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-5 (induced by source paper "provably robust boosted decision stumps and trees against adversarial attacks"):
  with members:
    - "parsimonious black box adversarial attacks via efficient combinatorial optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "robust decision trees against adversarial examples"
    - "simple black box adversarial attacks"
    - "scaling provable adversarial defenses"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-6 (induced by source paper "metric learning for adversarial robustness"):
  with members:
    - "hardness aware deep metric learning"
    - "are labels required for improving adversarial robustness"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "an analysis of the t sne algorithm for data visualization"
    - "formal security analysis of neural networks using symbolic intervals"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"

Connected through hyperedge HE-7 (induced by source paper "square attack a query efficient black box adversarial attack via random search"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "one pixel attack for fooling deep neural networks"
    - "sparse and imperceivable adversarial attacks"
    - "black box adversarial attacks with bayesian optimization"
    - "certified adversarial robustness via randomized smoothing"
    - "hopskipjumpattack a query efficient decision based attack"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "there are no bit parts for sign bits in black box attacks"

Connected through hyperedge HE-8 (induced by source paper "provable robustness against all adversarial l_p perturbations for p geq 1"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "transfer of adversarial robustness between perturbation types"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "scaling up the randomized gradient free adversarial attack reveals overestimation of robustness using established attacks"
    - "minimally distorted adversarial examples with a fast adaptive boundary attack"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "adversarial patch"

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)