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: "chinese typography transfer"
  Abstract: In this paper, we propose a new network architecture for Chinese typography transformation based on deep learning. The architecture consists of two sub-networks: (1)a fully convolutional network(FCN) aiming at transferring specified typography style to another in condition of preserving structure information; (2)an adversarial network aiming at generating more realistic strokes in some details. Unlike models proposed before 2012 relying on the complex segmentation of Chinese components or strokes, our model treats every Chinese character as an inseparable image, so pre-processing or post-preprocessing are abandoned. Besides, our model adopts end-to-end training without pre-trained used in other deep models. The experiments demonstrates that our model can synthesize realistic-looking target typography from any source typography both on printed style and handwriting style.

Connected through hyperedge HE-1 (induced by source paper "glyphgan style consistent font generation based on generative adversarial networks"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "semi supervised fusedgan for conditional image generation"
    - "word level font to font image translation using convolutional recurrent generative adversarial networks"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"
    - "aligngan learning to align cross domain images with conditional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "stefann scene text editor using font adaptive neural network"):
  with members:
    - "learning to read by spelling towards unsupervised text recognition"
    - "emerging from water underwater image color correction based on weakly supervised color transfer"
    - "edit probability for scene text recognition"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "east an efficient and accurate scene text detector"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "visual attribute transfer through deep image analogy"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-3 (induced by source paper "typeface completion with generative adversarial networks"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "word level font to font image translation using convolutional recurrent generative adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-4 (induced by source paper "pyramid embedded generative adversarial network for automated font generation"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning from simulated and unsupervised images through adversarial training"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "image to image translation with conditional adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "google s multilingual neural machine translation system enabling zero shot translation"

Connected through hyperedge HE-5 (induced by source paper "coconditional autoencoding adversarial networks for chinese font feature learning"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "generating handwritten chinese characters using cyclegan"
    - "unsupervised typography transfer"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "invertible conditional gans for image editing"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "multi content gan for few shot font style transfer"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "multi view data generation without view supervision"
    - "deep image prior"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "multi view image generation from a single view"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual attribute transfer through deep image analogy"

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)