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: "arbitrary style transfer with deep feature reshuffle"
  Abstract: This paper introduces a novel method by reshuffling deep features (i.e., permuting the spacial locations of a feature map) of the style image for arbitrary style transfer. We theoretically prove that our new style loss based on reshuffle connects both global and local style losses respectively used by most parametric and non-parametric neural style transfer methods. This simple idea can effectively address the challenging issues in existing style transfer methods. On one hand, it can avoid distortions in local style patterns, and allow semantic-level transfer, compared with neural parametric methods. On the other hand, it can preserve globally similar appearance to the style image, and avoid wash-out artifacts, compared with neural non-parametric methods. Based on the proposed loss, we also present a progressive feature-domain optimization approach. The experiments show that our method is widely applicable to various styles, and produces better quality than existing methods.

Connected through hyperedge HE-1 (induced by source paper "parameter free style projection for arbitrary style transfer"):
  with members:
    - "stylenas an empirical study of neural architecture search to uncover surprisingly fast end to end universal style transfer networks"
    - "fast universal style transfer for artistic and photorealistic rendering"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-2 (induced by source paper "p 2 gan efficient style transfer using single style image"):
  with members:
    - "attention aware multi stroke style transfer"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"
    - "visual attribute transfer through deep image analogy"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "stylebank an explicit representation for neural image style transfer"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "example guided style consistent image synthesis from semantic labeling"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "towards open set identity preserving face synthesis"
    - "semi parametric image synthesis"
    - "dual attention network for scene segmentation"
    - "densepose dense human pose estimation in the wild"

Connected through hyperedge HE-4 (induced by source paper "example guided scene image synthesis using masked spatial channel attention and patch based self supervision"):
  with members:
    - "example guided style consistent image synthesis from semantic labeling"
    - "semantic image synthesis with spatially adaptive normalization"
    - "photorealistic style transfer via wavelet transforms"
    - "video to video synthesis"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "multimodal unsupervised image to image translation"
    - "faceforensics a large scale video dataset for forgery detection in human faces"
    - "exemplar guided unsupervised image to image translation with semantic consistency"

Connected through hyperedge HE-5 (induced by source paper "face to parameter translation for game character auto creation"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "end to end 3d face reconstruction with deep neural networks"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-6 (induced by source paper "fast universal style transfer for artistic and photorealistic rendering"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep layer aggregation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-7 (induced by source paper "style mixer semantic aware multi style transfer network"):
  with members:
    - "attention aware multi stroke style transfer"
    - "arbitrary style transfer with style attentional networks"
    - "learning linear transformations for fast arbitrary style transfer"
    - "metastyle three way trade off among speed flexibility and quality in neural style transfer"
    - "the contextual loss for image transformation with non aligned data"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "non local neural networks"
    - "demystifying neural style transfer"

Connected through hyperedge HE-8 (induced by source paper "artistic glyph image synthesis via one stage few shot learning"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "figr few shot image generation with reptile"
    - "few shot unsupervised image to image translation"
    - "tet gan text effects transfer via stylization and destylization"
    - "the contextual loss for image transformation with non aligned data"
    - "image to image translation for cross domain disentanglement"
    - "style and content disentanglement in generative adversarial networks"
    - "drit diverse image to image translation via disentangled representations"

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)