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 "cross domain image classification through neural style transfer data augmentation"):
  Abstract: In particular, the lack of sufficient amounts of domain-specific data can reduce the accuracy of a classifier. In this paper, we explore the effects of style transfer-based data transformation on the accuracy of a convolutional neural network classifiers in the context of automobile detection under adverse winter weather conditions. The detection of automobiles under highly adverse weather conditions is a difficult task as such conditions present large amounts of noise in each image. The InceptionV2 architecture is trained on a composite dataset, consisting of either normal car image dataset , a mixture of normal and style transferred car images, or a mixture of normal car images and those taken at blizzard conditions, at a ratio of 80:20. All three classifiers are then tested on a dataset of car images taken at blizzard conditions and on vehicle-free snow landscape images. We evaluate and contrast the effectiveness of each classifier upon each dataset, and discuss the strengths and weaknesses of style-transfer based approaches to data augmentation.

Includes members:
  Member "a survey on face data augmentation":
    also connected through other hyperedges:
      (none)
  Member "data augmentation using gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "not enough data deep learning to the rescue")
  Member "stada style transfer as data augmentation":
    also connected through other hyperedges:
      (none)
  Member "gan augmentation augmenting training data using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on leveraging pretrained gans for limited data generation")
  Member "effects of degradations on deep neural network architectures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a possible reason for why data driven beats theory driven computer vision")
  Member "style augmentation data augmentation via style randomization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "introduction to camera pose estimation with deep learning")
  Member "domain adaptation for visual applications a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "demystifying neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")

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.CV(Computer Vision and Pattern Recognition)