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 "hold me tight influence of discriminative features on deep network boundaries"):
  Abstract: Important insights towards the explainability of neural networks and their properties reside in the formation of their decision boundaries. In this work, we borrow tools from the field of adversarial robustness and propose a new framework that permits to relate the features of the dataset with the distance of data samples to the decision boundary along specific directions. We demonstrate that the inductive bias of deep learning has the tendency to generate classification functions that are invariant along non-discriminative directions of the dataset. More surprisingly, we further show that training on small perturbations of the data samples are sufficient to completely change the decision boundary. This is actually the characteristic exploited by the so-called adversarial training to produce robust classifiers. Our general framework can be used to reveal the effect of specific dataset features on the macroscopic properties of deep models and to develop a better understanding of the successes and limitations of deep learning.

Includes members:
  Member "on the effectiveness of low frequency perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "frequency tuned universal adversarial attacks")
  Member "uniform convergence may be unable to explain generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "a fourier perspective on model robustness in computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "robustness via curvature regularization and vice versa":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "adversarial spheres":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "utilizing network properties to detect erroneous inputs")
  Member "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks")
  Member "on the decision boundary of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural language priors")

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)