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 "style transfer for light field photography"):
  Abstract: As light field images continue to increase in use and application, it becomes necessary to adapt existing image processing methods to this unique form of photography. In this paper we explore methods for applying neural style transfer to light field images. Feed-forward style transfer networks provide fast, high-quality results for monocular images, but no such networks exist for full light field images. Because of the size of these images, current light field data sets are small and are insufficient for training purely feed-forward style-transfer networks from scratch. Thus, it is necessary to adapt existing monocular style transfer networks in a way that allows for the stylization of each view of the light field while maintaining visual consistencies between views. Instead, the proposed method backpropagates the loss through the network, and the process is iterated to optimize (essentially overfit) the resulting stylization for a single light field image alone. The network architecture allows for the incorporation of pre-trained fast monocular stylization networks while avoiding the need for a large light field training set.

Includes members:
  Member "a content transformation block for image style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interactive neural style transfer with artists")
  Member "style transfer by relaxed optimal transport and self similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interactive neural style transfer with artists")
  Member "artistic style transfer for videos and spherical images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a content transformation block for image style transfer")
  Member "learning linear transformations for fast arbitrary style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "imaginenet restyling apps using neural style transfer")
  Member "stereoscopic neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gpu accelerated mobile multi view style transfer")
  Member "accurate light field depth estimation with superpixel regularization over partially occluded regions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stratified labeling for surface consistent parallax correction and occlusion completion")
  Member "characterizing and improving stability in neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep domain adversarial image generation for domain generalisation")

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)