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: "avatar net multi scale zero shot style transfer by feature decoration"
  Abstract: Zero-shot artistic style transfer is an important image synthesis problem aiming at transferring arbitrary style into content images. However, the trade-off between the generalization and efficiency in existing methods impedes a high quality zero-shot style transfer in real-time. In this paper, we resolve this dilemma and propose an efficient yet effective Avatar-Net that enables visually plausible multi-scale transfer for arbitrary style. The key ingredient of our method is a style decorator that makes up the content features by semantically aligned style features from an arbitrary style image, which does not only holistically match their feature distributions but also preserve detailed style patterns in the decorated features. By embedding this module into an image reconstruction network that fuses multi-scale style abstractions, the Avatar-Net renders multi-scale stylization for any style image in one feed-forward pass. We demonstrate the state-of-the-art effectiveness and efficiency of the proposed method in generating high-quality stylized images, with a series of applications include multiple style integration, video stylization and etc.

Connected through hyperedge HE-1 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "modulating image restoration with continual levels via adaptive feature modification layers"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"

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"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-3 (induced by source paper "regularized adaptation for stable and efficient continuous level learning"):
  with members:
    - "cfsnet toward a controllable feature space for image restoration"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "toward convolutional blind denoising of real photographs"
    - "the perception distortion tradeoff"
    - "residual dense network for image super resolution"
    - "dynamic net tuning the objective without re training for synthesis tasks"

Connected through hyperedge HE-4 (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"
    - "arbitrary style transfer with deep feature reshuffle"
    - "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-5 (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"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the contextual loss for image transformation with non aligned data"
    - "non local neural networks"
    - "demystifying neural style transfer"

Connected through hyperedge HE-6 (induced by source paper "conditional neural style transfer with peer regularized feature transform"):
  with members:
    - "few shot unsupervised image to image translation"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "the relativistic discriminator a key element missing from standard gan"
    - "a style aware content loss for real time hd style transfer"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "peernets exploiting peer wisdom against adversarial attacks"

Connected through hyperedge HE-7 (induced by source paper "diversified arbitrary style transfer via deep feature perturbation"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "learning linear transformations for fast arbitrary style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"

Connected through hyperedge HE-8 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "attention aware multi stroke style transfer"
    - "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"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"
    - "meta networks for 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)