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 "colored noise injection for training adversarially robust neural networks"):
  Abstract: Even though deep learning have shown unmatched performance on various tasks, neural networks has been shown to be vulnerable to small adversarial perturbation of the input which lead to significant performance degradation. In this work we extend the idea of adding independent Gaussian noise to weights and activation during adversarial training (PNI) to injection of colored noise for defense against common white-box and black-box attacks. We show that our approach outperforms PNI and various previous approaches in terms of adversarial accuracy on CIFAR-10 dataset. In addition, we provide an extensive ablation study of the proposed method justifying the chosen configurations.

Includes members:
  Member "training robust deep neural networks via adversarial noise propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "smoothed inference for adversarially trained models")
  Member "instance adaptive adversarial training improved accuracy tradeoffs in neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cat customized adversarial training for improved robustness")
  Member "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "defending against whitebox adversarial attacks via randomized discretization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an empirical evaluation of perturbation based defenses")
  Member "adversarial training with voronoi constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")
  Member "learning to defense by learning to attack":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "efficient defenses against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")

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)