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: "label smoothing and logit squeezing a replacement for adversarial training"
  Abstract: Adversarial training is one of the strongest defenses against adversarial attacks, but it requires adversarial examples to be generated for every mini-batch during optimization. The expense of producing these examples during training often precludes adversarial training from use on complex image datasets. In this study, we explore the mechanisms by which adversarial training improves classifier robustness, and show that these mechanisms can be effectively mimicked using simple regularization methods, including label smoothing and logit squeezing. Remarkably, using these simple regularization methods in combination with Gaussian noise injection, we are able to achieve strong adversarial robustness -- often exceeding that of adversarial training -- using no adversarial examples.

Connected through hyperedge HE-1 (induced by source paper "cat customized adversarial training for improved robustness"):
  with members:
    - "fast is better than free revisiting adversarial training"
    - "sign opt a query efficient hard label adversarial attack"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "certified adversarial robustness via randomized smoothing"
    - "convergence of adversarial training in overparametrized neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial robustness via adversarial label smoothing"

Connected through hyperedge HE-2 (induced by source paper "adversarial training for free"):
  with members:
    - "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"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "wasserstein adversarial regularization war on label noise"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "how does disagreement help generalization against label corruption"
    - "adversarial robustness via adversarial label smoothing"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "the devil of face recognition is in the noise"
    - "joint optimization framework for learning with noisy labels"
    - "dimensionality driven learning with noisy labels"
    - "learning to reweight examples for robust deep learning"

Connected through hyperedge HE-4 (induced by source paper "adversarial robustness via adversarial label smoothing"):
  with members:
    - "adversarial examples opportunities and challenges"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "darts deceiving autonomous cars with toxic signs"
    - "regularizing neural networks by penalizing confident output distributions"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "on the effectiveness of defensive distillation"
    - "towards evaluating the robustness of neural networks"
    - "adversarial machine learning at scale"

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)