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 "adversarial neural pruning with latent vulnerability suppression"):
  Abstract: Despite the remarkable performance of deep neural networks on various computer vision tasks, they are known to be highly susceptible to adversarial perturbations which makes it difficult to deploy them in real-world safety-critical applications. In this paper, we conjecture that the main cause of this adversarial vulnerability is the distortion in the latent feature space, and provide methods to effectively suppress them. Specifically, we define \textbf{vulnerability} for each latent feature and then propose a new loss for adversarial learning, \textbf{Vulnerability Suppression (VS)} loss, that aims to minimize the feature-level vulnerability during training. We further propose a Bayesian framework to prune features with high vulnerability, in order to reduce both vulnerability and loss on adversarial samples. We validate our \textbf{Adversarial Neural Pruning (ANP)} method on multiple benchmark datasets, on which it not only obtains state-of-the-art adversarial robustness but also improves the performance on clean examples, using only a fraction of the parameters used by the full network. Further qualitative analysis suggests that the improvements actually come from the suppression of feature-level vulnerability.

Includes members:
  Member "bridging adversarial robustness and gradient interpretability":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "concise explanations of neural networks using adversarial training")
  Member "theoretically principled trade off between robustness and accuracy":
    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 "adv bnn improved adversarial defense through robust bayesian neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "sparse dnns with improved adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on pruning adversarially robust neural networks")
  Member "a spectral view of adversarially robust features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "arcface additive angular margin loss for deep face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "towards the first adversarially robust neural network model on mnist":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)