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: "neural color transfer between images"
  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 optimize 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 transfers. 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 "mask guided portrait editing with conditional gans"):
  with members:
    - "faceshop deep sketch based face image editing"
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "semi parametric image synthesis"
    - "ganimation anatomically aware facial animation from a single image"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "vggface2 a dataset for recognising faces across pose and age"

Connected through hyperedge HE-2 (induced by source paper "deep exemplar based video colorization"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep video color propagation"
    - "learning blind video temporal consistency"
    - "spectral normalization for generative adversarial networks"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "the contextual loss for image transformation with non aligned data"
    - "pixel level semantics guided image colorization"

Connected through hyperedge HE-3 (induced by source paper "end to end time lapse video synthesis from a single outdoor image"):
  with members:
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "learning intrinsic image decomposition from watching the world"
    - "a closed form solution to photorealistic image stylization"
    - "deep sets"
    - "night to day image translation for retrieval based localization"
    - "drit diverse image to image translation via disentangled representations"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "when single event upset meets deep neural networks observations explorations and remedies"):
  with members:
    - "accuracy vs efficiency achieving both through fpga implementation aware neural architecture search"
    - "achieving super linear speedup across multi fpga for real time dnn inference"
    - "learning phase competition for traffic signal control"
    - "intrinsic image transformation via scale space decomposition"
    - "on the robustness of convolutional neural networks to internal architecture and weight perturbations"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "network in network"

Connected through hyperedge HE-5 (induced by source paper "hue net intensity based image to image translation with differentiable histogram loss functions"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "squared earth mover s distance based loss for training deep neural networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "using ai for economic upliftment of handicraft industry"):
  with members:
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "ear clipping based algorithms of generating high quality polygon triangulation"

Connected through hyperedge HE-7 (induced by source paper "improving shape deformation in unsupervised image to image translation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised creation of parameterized avatars"
    - "visual attribute transfer through deep image analogy"

Connected through hyperedge HE-8 (induced by source paper "deep video color propagation"):
  with members:
    - "the 2017 davis challenge on video object segmentation"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "videoset a large scale compressed video quality dataset based on jnd measurement"
    - "real time user guided image colorization with learned deep priors"
    - "video frame interpolation via adaptive separable convolution"
    - "universal style transfer via feature transforms"
    - "instance normalization the missing ingredient for fast stylization"

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)