human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sensitivity of deep convolutional networks to gabor noise"
  Abstract: Deep Convolutional Networks (DCNs) have been shown to be sensitive to Universal Adversarial Perturbations (UAPs): input-agnostic perturbations that fool a model on large portions of a dataset. These UAPs exhibit interesting visual patterns, but this phenomena is, as yet, poorly understood. Our work shows that visually similar procedural noise patterns also act as UAPs. In particular, we demonstrate that different DCN architectures are sensitive to Gabor noise patterns. This behaviour, its causes, and implications deserve further in-depth study.

Connected through hyperedge HE-1 (induced by source paper "testing robustness against unforeseen adversaries"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "spatially transformed adversarial examples"
    - "benchmarking neural network robustness to common corruptions and surface variations"

Connected through hyperedge HE-2 (induced by source paper "universal adversarial perturbations to understand robustness of texture vs shape biased training"):
  with members:
    - "improving the robustness of imagenet classifiers using elements of human visual cognition"
    - "adversarial training and robustness for multiple perturbations"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "transfer of adversarial robustness between perturbation types"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "universal adversarial training"
    - "generalizable data free objective for crafting universal adversarial perturbations"

Question: Please tell me which class the center node 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)