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: "photorealistic style transfer with screened poisson equation"
  Abstract: Recent work has shown impressive success in transferring painterly style to images. These approaches, however, fall short of photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. In this paper we propose an approach that takes as input a stylized image and makes it more photorealistic. It relies on the Screened Poisson Equation, maintaining the fidelity of the stylized image while constraining the gradients to those of the original input image. Our method is fast, simple, fully automatic and shows positive progress in making a stylized image photorealistic. Our results exhibit finer details and are less prone to artifacts than the state-of-the-art.

Connected through hyperedge HE-1 (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"
    - "deep photo style transfer"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-2 (induced by source paper "animating landscape self supervised learning of decoupled motion and appearance for single image video synthesis"):
  with members:
    - "manipulating attributes of natural scenes via hallucination"
    - "flow grounded spatial temporal video prediction from still images"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "personalized cinemagraphs using semantic understanding and collaborative learning"
    - "contextvp fully context aware video prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-3 (induced by source paper "two stream fcns to balance content and style for style transfer"):
  with members:
    - "evolvement constrained adversarial learning for video style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "a closed form solution to photorealistic image stylization"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "neural style transfer a review"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-4 (induced by source paper "high resolution network for photorealistic style transfer"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "laplacian steered neural style transfer"
    - "neural style transfer a review"

Connected through hyperedge HE-5 (induced by source paper "style transfer by relaxed optimal transport and self similarity"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "a closed form solution to photorealistic image stylization"
    - "the contextual loss for image transformation with non aligned data"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "visual attribute transfer through deep image analogy"
    - "perceptual losses for real time style transfer and super resolution"
    - "combining markov random fields and convolutional neural networks for image synthesis"

Connected through hyperedge HE-6 (induced by source paper "one shot mutual affine transfer for photorealistic stylization"):
  with members:
    - "sparse signal recovery via generalized entropy functions minimization"
    - "a closed form solution to photorealistic image stylization"
    - "learning linear transformations for fast arbitrary style transfer"
    - "mine mutual information neural estimation"
    - "deep photo style transfer"
    - "progressive color transfer with dense semantic correspondences"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-7 (induced by source paper "manipulating attributes of natural scenes via hallucination"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-8 (induced by source paper "a closed form solution to photorealistic image stylization"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "unsupervised image to image translation networks"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "laplacian steered neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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)