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: "style transfer via texture synthesis"
  Abstract: Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image, which is an artistic mixture of the two. Recent work on this problem adopting convolutional neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path toward handling the style transfer task, via the generalization of texture synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared with the CNN ones. In this paper, we propose a novel style transfer algorithm that extends the texture synthesis work of Kwatra et al. (2005), while aiming to get stylized images that are closer in quality to the CNN ones. We modify Kwatra’s algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.

Connected through hyperedge HE-1 (induced by source paper "gated gan adversarial gated networks for multi collection style transfer"):
  with members:
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-2 (induced by source paper "face to parameter translation for game character auto creation"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "arbitrary style transfer with deep feature reshuffle"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "multimodal style transfer via graph cuts"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "visual semantic reasoning for image text matching"
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the contextual loss for image transformation with non aligned data"
    - "demystifying neural style transfer"

Connected through hyperedge HE-4 (induced by source paper "semantic example guided image to image translation"):
  with members:
    - "asymmetric gan for unpaired image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "context aware text based binary image stylization and synthesis"):
  with members:
    - "visual attribute transfer through deep image analogy"
    - "stylebank an explicit representation for neural image style transfer"
    - "coherent online video style transfer"
    - "perceptual losses for real time style transfer and super resolution"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "controlling perceptual factors in neural style transfer"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "stroke controllable fast style transfer with adaptive receptive fields"):
  with members:
    - "stereoscopic neural style transfer"
    - "deep exemplar based colorization"
    - "decouple learning for parameterized image operators"
    - "context encoding for semantic segmentation"
    - "demystifying neural style transfer"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "the contextual loss for image transformation with non aligned data"):
  with members:
    - "learning to maintain natural image statistics"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "automatic semantic style transfer using deep convolutional neural networks and soft masks"

Connected through hyperedge HE-8 (induced by source paper "avatar net multi scale zero shot style transfer by feature decoration"):
  with members:
    - "stereoscopic neural style transfer"
    - "meta networks for neural style transfer"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "characterizing and improving stability in neural style transfer"

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)