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: "artistic style transfer for videos"
  Abstract: Techniques are disclosed herein for applying an artistic style extracted from one or more source images, e.g., paintings, to one or more target images. The extracted artistic style may then be stored as a plurality of layers in a neural network. In some embodiments, two or more stylized target images may be combined and stored as a stylized video sequence. The artistic style may be applied to the target images in the stylized video sequence using various optimization methods and/or pixel- and feature-based regularization techniques in a way that prevents excessive content pixel fluctuations between images and preserves smoothness in the assembled stylized video sequence. In other embodiments, a user may be able to semantically annotate locations of undesired artifacts in a target image, as well as portion(s) of a source image from which a style may be extracted and used to replace the undesired artifacts in the target image.

Connected through hyperedge HE-1 (induced by source paper "a content transformation block for image style transfer"):
  with members:
    - "adjustable real time style transfer"
    - "artistic style transfer for videos and spherical images"
    - "a style aware content loss for real time hd style transfer"
    - "generative adversarial networks an overview"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "diversified texture synthesis with feed forward networks"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "characterizing and improving stability in neural style transfer"

Connected through hyperedge HE-2 (induced by source paper "evolutionary image transition and painting using random walks"):
  with members:
    - "evolutionary diversity optimization using multi objective indicators"
    - "evolutionary image composition using feature covariance matrices"
    - "ponyge2 grammatical evolution in python"
    - "quasi random agents for image transition and animation"
    - "visual art inspired by the collective feeding behavior of sand bubbler crabs"
    - "perceptual losses for real time style transfer and super resolution"
    - "the evolutionary process of image transition in conjunction with box and strip mutation"
    - "controlling perceptual factors in neural style transfer"

Connected through hyperedge HE-3 (induced by source paper "is there tradeoff between spatial and temporal in video super resolution"):
  with members:
    - "two stream action recognition oriented video super resolution"
    - "on the classification distortion perception tradeoff"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "learning blind video temporal consistency"
    - "the perception distortion tradeoff"
    - "detail revealing deep video super resolution"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-4 (induced by source paper "gpu accelerated mobile multi view style transfer"):
  with members:
    - "stereoscopic neural style transfer"
    - "stereo magnification learning view synthesis using multiplane images"
    - "multi style generative network for real time transfer"
    - "coherent online video style transfer"
    - "instance normalization the missing ingredient for fast stylization"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "a learned representation for artistic style"

Connected through hyperedge HE-5 (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-6 (induced by source paper "deep exemplar based video colorization"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep video color propagation"
    - "learning blind video temporal consistency"
    - "spectral normalization for generative adversarial networks"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "the contextual loss for image transformation with non aligned data"
    - "pixel level semantics guided image colorization"

Connected through hyperedge HE-7 (induced by source paper "hypercon image to video model transfer for video to video translation tasks"):
  with members:
    - "deep flow guided video inpainting"
    - "deep video inpainting"
    - "copy and paste networks for deep video inpainting"
    - "video to video synthesis"
    - "learning blind video temporal consistency"
    - "a temporally aware interpolation network for video frame inpainting"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-8 (induced by source paper "transport based neural style transfer for smoke simulations"):
  with members:
    - "deep fluids a generative network for parameterized fluid simulations"
    - "coupled fluid density and motion from single views"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "tempogan a temporally coherent volumetric gan for super resolution fluid flow"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "neural style transfer a review"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "data driven synthesis of smoke flows with cnn based feature descriptors"

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)