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: "deepmovie using optical flow and deep neural networks to stylize movies"
  Abstract: A recent paper by Gatys et al. describes a method for rendering an image in the style of another image. First, they use convolutional neural network features to build a statistical model for the style of an image. Then they create a new image with the content of one image but the style statistics of another image. Here, we extend this method to render a movie in a given artistic style. The naive solution that independently renders each frame produces poor results because the features of the style move substantially from one frame to the next. The other naive method that initializes the optimization for the next frame using the rendered version of the previous frame also produces poor results because the features of the texture stay fixed relative to the frame of the movie instead of moving with objects in the scene. The main contribution of this paper is to use optical flow to initialize the style transfer optimization so that the texture features move with the objects in the video. Finally, we suggest a method to incorporate optical flow explicitly into the cost function.

Connected through hyperedge HE-1 (induced by source paper "mocycle gan unpaired video to video translation"):
  with members:
    - "video to video synthesis"
    - "reconet real time coherent video style transfer network"
    - "deep semantic hashing with generative adversarial networks"
    - "recycle gan unsupervised video retargeting"
    - "to create what you tell generating videos from captions"
    - "jointly localizing and describing events for dense video captioning"
    - "unsupervised attention guided image to image translation"
    - "multi style generative network for real time transfer"

Connected through hyperedge HE-2 (induced by source paper "evolvement constrained adversarial learning for video style transfer"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "decouple learning for parameterized image operators"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"

Connected through hyperedge HE-3 (induced by source paper "stereoscopic neural style transfer"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "continuous 3d label stereo matching using local expansion moves"
    - "towards open set identity preserving face synthesis"
    - "learning selfie friendly abstraction from artistic style images"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "characterizing and improving stability in neural style transfer"
    - "revisiting deep intrinsic image decompositions"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-4 (induced by source paper "reconet real time coherent video style transfer network"):
  with members:
    - "artistic style transfer for videos and spherical images"
    - "stereoscopic neural style transfer"
    - "deep photo style transfer"
    - "characterizing and improving stability in neural style transfer"
    - "visual attribute transfer through deep image analogy"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-5 (induced by source paper "contained neural style transfer for decorated logo generation"):
  with members:
    - "neural style transfer a review"
    - "perceptual losses for real time style transfer and super resolution"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "fast patch based style transfer of arbitrary style"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "image to image translation with conditional adversarial networks"
    - "preserving color in neural artistic style transfer"

Connected through hyperedge HE-6 (induced by source paper "coherent online video style transfer"):
  with members:
    - "diversified texture synthesis with feed forward networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "optical flow estimation using a spatial pyramid network"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "combining markov random fields and convolutional neural networks for image synthesis"

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)