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: "deep exemplar based colorization"
  Abstract: We propose the first deep learning approach for exemplar-based local colorization. Given a reference color image, our convolutional neural network directly maps a grayscale image to an output colorized image. Rather than using hand-crafted rules as in traditional exemplar-based methods, our end-to-end colorization network learns how to select, propagate, and predict colors from the large-scale data. The approach performs robustly and generalizes well even when using reference images that are unrelated to the input grayscale image. More importantly, as opposed to other learning-based colorization methods, our network allows the user to achieve customizable results by simply feeding different references. In order to further reduce manual effort in selecting the references, the system automatically recommends references with our proposed image retrieval algorithm, which considers both semantic and luminance information. The colorization can be performed fully automatically by simply picking the top reference suggestion. Our approach is validated through a user study and favorable quantitative comparisons to the-state-of-the-art methods. Furthermore, our approach can be naturally extended to video colorization. Our code and models will be freely available for public use.

Connected through hyperedge HE-1 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution"):
  with members:
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "real time user guided image colorization with learned deep priors"
    - "adversarial ranking for language generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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"
    - "the contextual loss for image transformation with non aligned data"
    - "pixel level semantics guided image colorization"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-3 (induced by source paper "pixelated semantic colorization"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "pixel level semantics guided image colorization"
    - "tracking emerges by colorizing videos"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-4 (induced by source paper "learning digital camera pipeline for extreme low light imaging"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "toward convolutional blind denoising of real photographs"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to see in the dark"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "learned perceptual image enhancement"
    - "deep bilateral learning for real time image enhancement"

Connected through hyperedge HE-5 (induced by source paper "chromagan an adversarial approach for picture colorization"):
  with members:
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "real time user guided image colorization with learned deep priors"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-6 (induced by source paper "fully automatic video colorization with self regularization and diversity"):
  with members:
    - "deep video color propagation"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "switchable temporal propagation network"
    - "tracking emerges by colorizing videos"
    - "colorization as a proxy task for visual understanding"
    - "real time user guided image colorization with learned deep priors"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-7 (induced by source paper "stroke controllable fast style transfer with adaptive receptive fields"):
  with members:
    - "stereoscopic neural style transfer"
    - "decouple learning for parameterized image operators"
    - "context encoding for semantic segmentation"
    - "demystifying neural style transfer"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deformable convolutional networks"
    - "style transfer via texture synthesis"

Connected through hyperedge HE-8 (induced by source paper "decouple learning for parameterized image operators"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "stereoscopic neural style transfer"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "density aware single image de raining using a multi stream dense network"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "global versus localized generative adversarial nets"
    - "temporal context network for activity localization in videos"

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)