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 "the curious case of adversarially robust models more data can help double descend or hurt generalization"):
  Abstract: Despite remarkable success, deep neural networks are sensitive to human-imperceptible small perturbations on the data and could be adversarially misled to produce incorrect or even dangerous predictions. To circumvent these issues, practitioners introduced adversarial training to produce adversarially robust models whose predictions are robust to small perturbations to the data. It is widely believed that more training data will help adversarially robust models generalize better on the test data. In this paper, however, we challenge this conventional belief and show that more training data could hurt the generalization of adversarially robust models for the linear classification problem. We identify three regimes based on the strength of the adversary. In the weak adversary regime, more data improves the generalization of adversarially robust models. In the medium adversary regime, with more training data, the generalization loss exhibits a double descent curve. This implies that in this regime, there is an intermediate stage where more training data hurts their generalization. In the strong adversary regime, more data almost immediately causes the generalization error to increase.

Includes members:
  Member "more data can expand the generalization gap between adversarially robust and standard models":
    also connected through other hyperedges:
      (none)
  Member "a new perspective for flexible feature gathering in scene text recognition via character anchor pooling":
    also connected through other hyperedges:
      (none)
  Member "lower bounds for adversarially robust pac learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models")
  Member "two models of double descent for weak features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "improved sample complexities for deep networks and robust classification via an all layer margin":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "deep double descent where bigger models and more data hurt":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "lower bounds on adversarial robustness from optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models")
  Member "vc classes are adversarially robustly learnable but only improperly":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "more data can expand the generalization gap between adversarially robust and standard models")

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)