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 "improving the affordability of robustness training for dnns"):
  Abstract: Projected Gradient Descent (PGD) based adversarial training has become one of the most prominent methods for building robust deep neural network models. However, the computational complexity associated with this approach, due to the maximization of the loss function when finding adversaries, is a longstanding problem and may be prohibitive when using larger and more complex models. In this paper, we propose a modification of the PGD method for adversarial training and demonstrate that models can be trained much more efficiently without any loss in accuracy on natural and adversarial samples. We argue that the initial phase of adversarial training is redundant and can be replaced with natural training thereby increasing the computational efficiency significantly. We support our argument with insights on the nature of the adversaries and their relative strength during the training process. We show that our proposed method can reduce the training time to up to 38\% of the original training time with comparable model accuracy and generalization on various strengths of adversarial attacks.

Includes members:
  Member "improving adversarial robustness via promoting ensemble diversity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "exploring the hyperparameter landscape of adversarial robustness":
    also connected through other hyperedges:
      (none)
  Member "adversarial training for free":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "using pre training can improve model robustness and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "adversarially robust transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving the adversarial robustness of transfer learning via noisy feature distillation")
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "curriculum adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mga momentum gradient attack on network")
  Member "towards the first adversarially robust neural network model on mnist":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")

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)