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: "progressive color transfer with dense semantic correspondences"
  Abstract: We propose a new algorithm for color transfer between images that have perceptually similar semantic structures. We aim to achieve a more accurate color transfer that leverages semantically-meaningful dense correspondence between images. To accomplish this, our algorithm uses neural representations for matching. Additionally, the color transfer should be spatially variant and globally coherent. Therefore, our algorithm optimizes a local linear model for color transfer satisfying both local and global constraints. Our proposed approach jointly optimizes matching and color transfer, adopting a coarse-to-fine strategy. The proposed method can be successfully extended from one-to-one to one-to-many color transfer. The latter further addresses the problem of mismatching elements of the input image. We validate our proposed method by testing it on a large variety of image content.

Connected through hyperedge HE-1 (induced by source paper "high resolution daytime translation without domain labels"):
  with members:
    - "end to end time lapse video synthesis from a single outdoor image"
    - "few shot unsupervised image to image translation"
    - "deep high resolution representation learning for human pose estimation"
    - "cgans with projection discriminator"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-2 (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"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"

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)