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: "word level font to font image translation using convolutional recurrent generative adversarial networks"
  Abstract: Conversion of one font to another font is very useful in real life applications. In this paper, we propose a Convolutional Recurrent Generative model to solve the word level font transfer problem. Our network is able to convert the font style of any printed text images from its current font to the required font. The network is trained end-to-end for the complete word images. Thus it eliminates the necessary pre-processing steps, like character segmentations. We extend our model to conditional setting that helps to learn one-to-many mapping function. We employ a novel convolutional recurrent model architecture in the Generator that efficiently deals with the word images of arbitrary width. It also helps to maintain the consistency of the final images after concatenating the generated image patches of target font. Besides, the Generator and the Discriminator network, we employ a Classification network to classify the generated word images of converted font style to their subsequent font categories. Most of the earlier works related to image translation are performed on square images. Our proposed architecture is the first work which can handle images of varying widths. Word images generally have varying width depending on the number of characters present. Hence, we test our model on a synthetically generated font dataset. We compare our method with some of the state-of-the-art methods for image translation. The superior performance of our network on the same dataset proves the ability of our model to learn the font distributions.

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"
    - "chinese typography transfer"
    - "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 "selective style transfer for text"):
  with members:
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "east an efficient and accurate scene text detector"
    - "improving text proposals for scene images with fully convolutional networks"
    - "deep photo style transfer"
    - "automatic semantic style transfer using deep convolutional neural networks and soft masks"
    - "multi content gan for few shot font style transfer"
    - "perceptual losses for real time style transfer and super resolution"

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"
    - "chinese typography transfer"
    - "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"

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)