human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "overfitting in adversarially robust deep learning"):
  Abstract: It is common practice in deep learning to use overparameterized networks and train for as long as possible; there are numerous studies that show, both theoretically and empirically, that such practices surprisingly do not unduly harm the generalization performance of the classifier. In this paper, we empirically study this phenomenon in the setting of adversarially trained deep networks, which are trained to minimize the loss under worst-case adversarial perturbations. We find that overfitting to the training set does in fact harm robust performance to a very large degree in adversarially robust training across multiple datasets (SVHN, CIFAR-10, CIFAR-100, and ImageNet) and perturbation models ($\ell_\infty$ and $\ell_2$). Based upon this observed effect, we show that the performance gains of virtually all recent algorithmic improvements upon adversarial training can be matched by simply using early stopping. We also show that effects such as the double descent curve do still occur in adversarially trained models, yet fail to explain the observed overfitting. Finally, we study several classical and modern deep learning remedies for overfitting, including regularization and data augmentation, and find that no approach in isolation improves significantly upon the gains achieved by early stopping. All code for reproducing the experiments as well as pretrained model weights and training logs can be found at this https URL.

Includes members:
  Member "fast is better than free revisiting adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "adversarial training and robustness for multiple perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast is better than free revisiting adversarial training")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "deep double descent where bigger models and more data hurt":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert of theseus compressing bert by progressive module replacing")
  Member "wasserstein adversarial examples via projected sinkhorn iterations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")
  Member "are labels required for improving adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "adversarially robust generalization just requires more unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "me net towards effective adversarial robustness with matrix estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast is better than free revisiting adversarial training")

Question: Please tell me which class the source paper 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)