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: "auto encoder guided gan for chinese calligraphy synthesis"
  Abstract: In this paper, we investigate the Chinese calligraphy synthesis problem: synthesizing Chinese calligraphy images with specified style from standard font(eg. Hei font) images (Fig. 1(a)). Recent works mostly follow the stroke extraction and assemble pipeline which is complex in the process and limited by the effect of stroke extraction. We treat the calligraphy synthesis problem as an image-to-image translation problem and propose a deep neural network based model which can generate calligraphy images from standard font images directly. Besides, we also construct a large scale benchmark that contains various styles for Chinese calligraphy synthesis. We evaluate our method as well as some baseline methods on the proposed dataset, and the experimental results demonstrate the effectiveness of our proposed model.

Connected through hyperedge HE-1 (induced by source paper "ganwriting content conditioned generation of styled handwritten word images"):
  with members:
    - "adversarial generation of handwritten text images conditioned on sequences"
    - "few shot unsupervised image to image translation"
    - "unsupervised writer adaptation for synthetic to real handwritten word recognition"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating handwritten chinese characters using cyclegan"

Connected through hyperedge HE-2 (induced by source paper "neural style difference transfer and its application to font generation"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "automatic generation of typographic font from small font subset"):
  with members:
    - "awesome typography statistics based text effects transfer"

Connected through hyperedge HE-4 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "video prediction with appearance and motion conditions"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"

Connected through hyperedge HE-5 (induced by source paper "editing text in the wild"):
  with members:
    - "progressive pose attention transfer for person image generation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "stefann scene text editor using font adaptive neural network"
    - "synthesizing images of humans in unseen poses"
    - "spectral normalization for generative adversarial networks"
    - "tet gan text effects transfer via stylization and destylization"
    - "scene text detection and recognition the deep learning era"
    - "ensnet ensconce text in the wild"

Connected through hyperedge HE-6 (induced by source paper "artistic glyph image synthesis via one stage few shot learning"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "figr few shot image generation with reptile"
    - "few shot unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "tet gan text effects transfer via stylization and destylization"
    - "the contextual loss for image transformation with non aligned data"
    - "image to image translation for cross domain disentanglement"
    - "style and content disentanglement in generative adversarial networks"

Connected through hyperedge HE-7 (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"
    - "chinese typography transfer"
    - "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-8 (induced by source paper "handwritten chinese font generation with collaborative stroke refinement"):
  with members:
    - "learning discriminative video representations using adversarial perturbations"
    - "a style aware content loss for real time hd style transfer"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "separating style and content for generalized style transfer"

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)