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: "high frequency component helps explain the generalization of convolutional neural networks"
  Abstract: We investigate the relationship between the frequency spectrum of image data and the generalization behavior of convolutional neural networks (CNN). We first notice CNN's ability in capturing the high-frequency components of images. These high-frequency components are almost imperceptible to a human. Thus the observation leads to multiple hypotheses that are related to the generalization behaviors of CNN, including a potential explanation for adversarial examples, a discussion of CNN's trade-off between robustness and accuracy, and some evidence in understanding training heuristics. Our observation also immediately inspire methods related to the adversarial attack and defense methods.

Connected through hyperedge HE-1 (induced by source paper "frequency tuned universal adversarial attacks"):
  with members:
    - "a survey of black box adversarial attacks on computer vision models"
    - "on the effectiveness of low frequency perturbations"
    - "a geometry inspired decision based attack"
    - "defending against universal attacks through selective feature regeneration"
    - "simple black box adversarial attacks"
    - "a study of black box adversarial attacks in computer vision"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "universal adversarial training"

Connected through hyperedge HE-2 (induced by source paper "sketchtransfer a challenging new task for exploring detail invariance and the abstractions learned by deep networks"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "kuronet pre modern japanese kuzushiji character recognition with deep learning"
    - "recurrent independent mechanisms"
    - "virtual mixup training for unsupervised domain adaptation"
    - "graphmix regularized training of graph neural networks for semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"

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.CV(Computer Vision and Pattern Recognition)