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: "attention aware multi stroke style transfer"
  Abstract: Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level of detail via different brush strokes. In this paper, we tackle these limitations by developing an attention-aware multi-stroke style transfer model. We first propose to assemble self-attention mechanism into a style-agnostic reconstruction autoencoder framework, from which the attention map of a content image can be derived. By performing multi-scale style swap on content features and style features, we produce multiple feature maps reflecting different stroke patterns. A flexible fusion strategy is further presented to incorporate the salient characteristics from the attention map, which allows integrating multiple stroke patterns into different spatial regions of the output image harmoniously. We demonstrate the effectiveness of our method, as well as generate comparable stylized images with multiple stroke patterns against the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "p 2 gan efficient style transfer using single style image"):
  with members:
    - "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"
    - "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-2 (induced by source paper "fake generated painting detection via frequency analysis"):
  with members:
    - "face x ray for more general face forgery detection"
    - "gated gan adversarial gated networks for multi collection style transfer"
    - "distinguishing computer generated graphics from natural images based on sensor pattern noise and deep learning"
    - "fake colorized image detection"
    - "do gans leave artificial fingerprints"
    - "detection of deep network generated images using disparities in color components"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "style mixer semantic aware multi style transfer network"):
  with members:
    - "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"
    - "arbitrary style transfer with deep feature reshuffle"
    - "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-4 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "difnet semantic segmentation by diffusion networks"
    - "style separation and synthesis via generative adversarial networks"
    - "deep back projection networks for super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"
    - "meta networks for neural style transfer"

Connected through hyperedge HE-5 (induced by source paper "cartoonrenderer an instance based multi style cartoon image translator"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "texture networks feed forward synthesis of textures and stylized images"

Connected through hyperedge HE-6 (induced by source paper "pair wise exchangeable feature extraction for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "etnet error transition network for arbitrary style transfer"
    - "adversarial learning with local coordinate coding"
    - "arbitrary style transfer with style attentional networks"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "style separation and synthesis via generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "efanet exchangeable feature alignment network for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "etnet error transition network for arbitrary style transfer"
    - "arbitrary style transfer with style attentional networks"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "style separation and synthesis via generative adversarial networks"
    - "dida disentangled synthesis for domain adaptation"
    - "avatar net multi scale zero shot style transfer by feature decoration"

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)