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: "outline colorization through tandem adversarial networks"
  Abstract: When creating digital art, coloring and shading are often time consuming tasks that follow the same general patterns. A solution to automatically colorize raw line art would have many practical applications. We propose a setup utilizing two networks in tandem: a color prediction network based only on outlines, and a shading network conditioned on both outlines and a color scheme. We present processing methods to limit information passed in the color scheme, improving generalization. Finally, we demonstrate natural-looking results when colorizing outlines from scratch, as well as from a messy, user-defined color scheme.

Connected through hyperedge HE-1 (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"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (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"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "lucss language based user customized colourization of scene sketches"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "sketchyscene richly annotated scene sketches"
    - "texturegan controlling deep image synthesis with texture patches"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "a neural representation of sketch drawings"

Connected through hyperedge HE-4 (induced by source paper "user guided deep anime line art colorization with conditional adversarial networks"):
  with members:
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comicolorization semi automatic manga colorization"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pixcolor pixel recursive colorization"
    - "dilated residual networks"
    - "real time user guided image colorization with learned deep priors"

Connected through hyperedge HE-5 (induced by source paper "interpreting models by allowing to ask"):
  with members:
    - "interpretable and globally optimal prediction for textual grounding using image concepts"
    - "pixcolor pixel recursive colorization"
    - "cgan based manga colorization using a single training image"
    - "unsupervised learning of disentangled and interpretable representations from sequential data"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "the mythos of model interpretability"
    - "u net convolutional networks for biomedical image segmentation"
    - "the fast bilateral solver"

Connected through hyperedge HE-6 (induced by source paper "pixel level semantics guided image colorization"):
  with members:
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "probabilistic image colorization"
    - "learning representations for automatic colorization"
    - "infrared colorization using deep convolutional neural networks"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-7 (induced by source paper "real time user guided image colorization with learned deep priors"):
  with members:
    - "learning representations for automatic colorization"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "scribbler controlling deep image synthesis with sketch and color"
    - "generative visual manipulation on the natural image manifold"
    - "deep interactive object selection"

Connected through hyperedge HE-8 (induced by source paper "pixcolor pixel recursive colorization"):
  with members:
    - "fast generation for convolutional autoregressive models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "pixel recursive super resolution"
    - "probabilistic image colorization"
    - "learning representations for automatic colorization"
    - "conditional image generation with pixelcnn decoders"

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)