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 "reconet real time coherent video style transfer network"):
  Abstract: Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos. Some video style transfer models have been proposed to improve temporal consistency, yet they fail to guarantee fast processing speed, nice perceptual style quality and high temporal consistency at the same time. In this paper, we propose a novel real-time video style transfer model, ReCoNet, which can generate temporally coherent style transfer videos while maintaining favorable perceptual styles. A novel luminance warping constraint is added to the temporal loss at the output level to capture luminance changes between consecutive frames and increase stylization stability under illumination effects. We also propose a novel feature-map-level temporal loss to further enhance temporal consistency on traceable objects. Experimental results indicate that our model exhibits outstanding performance both qualitatively and quantitatively.

Includes members:
  Member "artistic style transfer for videos and spherical images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a content transformation block for image style transfer")
  Member "stereoscopic neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "style transfer for light field photography")
  Member "deep photo style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "parameter free style projection for arbitrary style transfer")
  Member "characterizing and improving stability in neural style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient semantic video segmentation with per frame inference")
  Member "visual attribute transfer through deep image analogy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "stylebank an explicit representation for neural image style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "coherent online video style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "flownet 2 0 evolution of optical flow estimation with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)