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: "cgan based manga colorization using a single training image"
  Abstract: The Japanese comic format known as Manga is popular all over the world. It is traditionally produced in black and white, and colorization is time consuming and costly. Automatic colorization methods generally rely on greyscale values, which are not present in manga. Furthermore, due to copyright protection, colorized manga available for training is scarce. We propose a manga colorization method based on conditional Generative Adversarial Networks (cGAN). Unlike previous cGAN approaches that use many hundreds or thousands of training images, our method requires only a single colorized reference image for training, avoiding the need of a large dataset. Colorizing manga using cGANs can produce blurry results with artifacts, and the resolution is limited. We therefore also propose a method of segmentation and color-correction to mitigate these issues. The final results are sharp, clear, and in high resolution, and stay true to the character's original color scheme.

Connected through hyperedge HE-1 (induced by source paper "adversarial colorization of icons based on structure and color conditions"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "conditional image to image translation"
    - "semi supervised fusedgan for conditional image generation"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "unsupervised diverse colorization via generative adversarial networks"

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"
    - "comicolorization semi automatic manga colorization"

Connected through hyperedge HE-3 (induced by source paper "semi supervised image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a closed form solution to photorealistic image stylization"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "unsupervised image to image translation networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "least squares generative adversarial networks"

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"
    - "outline colorization through tandem adversarial networks"
    - "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"

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.GR(Graphics)