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: "real time user guided image colorization with learned deep priors"
  Abstract: We propose a deep learning approach for user-guided image colorization. The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN). Rather than using hand-defined rules, the network propagates user edits by fusing low-level cues along with high-level semantic information, learned from large-scale data. We train on a million images, with simulated user inputs. To guide the user towards efficient input selection, the system recommends likely colors based on the input image and current user inputs. The colorization is performed in a single feed-forward pass, enabling real-time use. Even with randomly simulated user inputs, we show that the proposed system helps novice users quickly create realistic colorizations, and offers large improvements in colorization quality with just a minute of use. In addition, we demonstrate that the framework can incorporate other user "hints" to the desired colorization, showing an application to color histogram transfer. Our code and models are available at this https URL

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"
    - "deep exemplar based colorization"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "adversarial ranking for language generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "learning to shade hand drawn sketches"):
  with members:
    - "im2pencil controllable pencil illustration from photographs"
    - "tag2pix line art colorization using text tag with secat and changing loss"
    - "single image portrait relighting"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comicolorization semi automatic manga colorization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mastering sketching adversarial augmentation for structured prediction"

Connected through hyperedge HE-3 (induced by source paper "unsupervised image to image translation with self attention networks"):
  with members:
    - "arbitrary style transfer with style attentional networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "u gat it unsupervised generative attentional networks with adaptive layer instance normalization for image to image translation"):
  with members:
    - "group normalization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unsupervised attention guided image to image translation"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "drit diverse image to image translation via disentangled representations"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "freihand a dataset for markerless capture of hand pose and shape from single rgb images"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "learning joint reconstruction of hands and manipulated objects"
    - "3d hand shape and pose from images in the wild"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "large scale multiview 3d hand pose dataset"
    - "learning to estimate 3d hand pose from single rgb images"

Connected through hyperedge HE-7 (induced by source paper "pixelated semantic colorization"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "deep exemplar based colorization"
    - "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"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-8 (induced by source paper "guided image to image translation with bi directional feature transformation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "multimodal unsupervised image to image translation"
    - "learning blind video temporal consistency"
    - "free form image inpainting with gated convolution"
    - "dense pose transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

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)