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: "visual attribute transfer through deep image analogy"
  Abstract: We propose a new technique for visual attribute transfer across images that may have very different appearance but have perceptually similar semantic structure. By visual attribute transfer, we mean transfer of visual information (such as color, tone, texture, and style) from one image to another. For example, one image could be that of a painting or a sketch while the other is a photo of a real scene, and both depict the same type of scene. #R##N#Our technique finds semantically-meaningful dense correspondences between two input images. To accomplish this, it adapts the notion of "image analogy" with features extracted from a Deep Convolutional Neutral Network for matching; we call our technique Deep Image Analogy. A coarse-to-fine strategy is used to compute the nearest-neighbor field for generating the results. We validate the effectiveness of our proposed method in a variety of cases, including style/texture transfer, color/style swap, sketch/painting to photo, and time lapse.

Connected through hyperedge HE-1 (induced by source paper "collaborative distillation for ultra resolution universal style transfer"):
  with members:
    - "relational knowledge distillation"
    - "multimodal style transfer via graph cuts"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unpaired high resolution and scalable style transfer using generative adversarial networks"
    - "a style aware content loss for real time hd style transfer"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (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"
    - "arbitrary style transfer with deep feature reshuffle"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "imaginenet restyling apps using neural style transfer"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "learning linear transformations for fast arbitrary style transfer"
    - "demystifying neural style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "3d carigan an end to end solution to 3d caricature generation from face photos"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "warpgan automatic caricature generation"
    - "alive caricature from 2d to 3d"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"

Connected through hyperedge HE-5 (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"
    - "arbitrary style transfer with deep feature reshuffle"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "neural style transfer a review"
    - "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-6 (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"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-7 (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-8 (induced by source paper "content and colour distillation for learning image translations with the spatial profile loss"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "improving gan training via binarized representation entropy bre regularization"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the contextual loss for image transformation with non aligned data"
    - "which training methods for gans do actually converge"
    - "perceptual adversarial networks for image to image transformation"

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)