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: "comicolorization semi automatic manga colorization"
  Abstract: We developed "Comicolorization", a semi-automatic colorization system for manga images. Given a monochrome manga and reference images as inputs, our system generates a plausible color version of the manga. This is the first work to address the colorization of an entire manga title (a set of manga pages). Our method colorizes a whole page (not a single panel) semi-automatically, with the same color for the same character across multiple panels. To colorize the target character by the color from the reference image, we extract a color feature from the reference and feed it to the colorization network to help the colorization. Our approach employs adversarial loss to encourage the effect of the color features. Optionally, our tool allows users to revise the colorization result interactively. By feeding the color features to our deep colorization network, we accomplish colorization of the entire manga using the desired colors for each panel.

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"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mastering sketching adversarial augmentation for structured prediction"

Connected through hyperedge HE-2 (induced by source paper "tag2pix line art colorization using text tag with secat and changing loss"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "learning to color from language"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "semantic image synthesis via adversarial learning"
    - "language based image editing with recurrent attentive models"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "towards the automatic anime characters creation with generative adversarial networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "a survey of comics research in computer science"):
  with members:
    - "using posters to recommend anime and mangas in a cold start scenario"
    - "sketch based manga retrieval using manga109 dataset"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "obamanet photo realistic lip sync from text"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "style transfer for anime sketches with enhanced residual u net and auxiliary classifier gan"
    - "visual to sound generating natural sound for videos in the wild"

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)