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: "universal style transfer via feature transforms"
  Abstract: Universal style transfer aims to transfer arbitrary visual styles to content images. Existing feed-forward based methods, while enjoying the inference efficiency, are mainly limited by inability of generalizing to unseen styles or compromised visual quality. In this paper, we present a simple yet effective method that tackles these limitations without training on any pre-defined styles. The key ingredient of our method is a pair of feature transforms, whitening and coloring, that are embedded to an image reconstruction network. The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer. We demonstrate the effectiveness of our algorithm by generating high-quality stylized images with comparisons to a number of recent methods. We also analyze our method by visualizing the whitened features and synthesizing textures via simple feature coloring.

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 "transfer learning from synthetic to real noise denoising with adaptive instance normalization"):
  with members:
    - "toward real world single image super resolution a new benchmark and a new model"
    - "noise flow noise modeling with conditional normalizing flows"
    - "real image denoising with feature attention"
    - "semantic image synthesis with spatially adaptive normalization"
    - "suppressing model overfitting for image super resolution networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "image super resolution using very deep residual channel attention networks"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "interactive neural style transfer with artists"):
  with members:
    - "a content transformation block for image style transfer"
    - "style transfer by relaxed optimal transport and self similarity"
    - "dialog on a canvas with a machine"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "wasserstein style transfer"
    - "multimodal style transfer via graph cuts"
    - "a style aware content loss for real time hd style transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-6 (induced by source paper "on demand solid texture synthesis using deep 3d networks"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep image prior"
    - "diversified texture synthesis with feed forward networks"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "learning texture manifolds with the periodic spatial gan"

Connected through hyperedge HE-7 (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"
    - "visual attribute transfer through deep image analogy"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-8 (induced by source paper "style transfer for light field photography"):
  with members:
    - "a content transformation block for image style transfer"
    - "style transfer by relaxed optimal transport and self similarity"
    - "artistic style transfer for videos and spherical images"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "characterizing and improving stability in neural style transfer"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture 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)