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: "automatic generation of typographic font from small font subset"
  Abstract: The automated generation of fonts containing a large number of characters is in high demand. For example, a typical Japanese font requires over 1000 characters. Unfortunately, professional typographers create the majority of fonts, resulting in significant financial and time investments for font generation. The main contribution of this article is the development of a method that automatically generates a target typographic font containing thousands of characters, from a small subset of character images in the target font. We generate characters other than the subset so that a complete font is obtained. We propose a novel font generation method with the capability to deal with various fonts, including a font composed of distinctive strokes, which are difficult for existing methods to handle. We demonstrated the proposed method by generating 2965 characters in 47 fonts. Moreover, objective and subjective evaluations verified that the generated characters are similar to the original characters.

Connected through hyperedge HE-1 (induced by source paper "neural style difference transfer and its application to font generation"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "the contextual loss for image transformation with non aligned data"
    - "pyramid embedded generative adversarial network for automated font generation"
    - "contained neural style transfer for decorated logo generation"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep photo style transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"

Connected through hyperedge HE-2 (induced by source paper "character independent font identification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "glyphgan style consistent font generation based on generative adversarial networks"
    - "serif or sans visual font analytics on book covers and online advertisements"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "exploring disentangled feature representation beyond face identification"
    - "learning features for offline handwritten signature verification using deep convolutional neural networks"
    - "font identification in historical documents using active learning"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-3 (induced by source paper "glyphgan style consistent font generation based on generative adversarial networks"):
  with members:
    - "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"
    - "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-4 (induced by source paper "fontgan a unified generative framework for chinese character stylization and de stylization"):
  with members:
    - "global and local consistent wavelet domain age synthesis"
    - "generating handwritten chinese characters using cyclegan"
    - "drit diverse image to image translation via disentangled representations"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "separating style and content for generalized style transfer"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-5 (induced by source paper "pyramid embedded generative adversarial network for automated font generation"):
  with members:
    - "chinese typography transfer"
    - "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"

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)