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 "towards noise robust neural networks via progressive adversarial training"):
  Abstract: Adversarial examples, intentionally designed inputs tending to mislead deep neural networks, have attracted great attention in the past few years. Although a series of defense strategies have been developed and achieved encouraging model robustness, most of them are still vulnerable to the more commonly witnessed corruptions, e.g., Gaussian noise, blur, etc., in the real world. In this paper, we theoretically and empirically discover the fact that there exists an inherent connection between adversarial robustness and corruption robustness. Based on the fundamental discovery, this paper further proposes a more powerful training method named Progressive Adversarial Training (PAT) that adds diversified adversarial noises progressively during training, and thus obtains robust model against both adversarial examples and corruptions through higher training data complexity. Meanwhile, we also theoretically find that PAT can promise better generalization ability. Experimental evaluation on MNIST, CIFAR-10 and SVHN show that PAT is able to enhance the robustness and generalization of the state-of-the-art network structures, performing comprehensively well compared to various augmentation methods. Moreover, we also propose Mixed Test to evaluate model generalization ability more fairly.

Includes members:
  Member "generalizing to unseen domains via adversarial data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "adversarial examples in the physical world":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "robustness and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding generalization in deep learning via tensor methods")

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)