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: "awesome typography statistics based text effects transfer"
  Abstract: In this work, we explore the problem of generating fantastic special-effects for the typography. It is quite challenging due to the model diversities to illustrate varied text effects for different characters. To address this issue, our key idea is to exploit the analytics on the high regularity of the spatial distribution for text effects to guide the synthesis process. Specifically, we characterize the stylized patches by their normalized positions and the optimal scales to depict their style elements. Our method first estimates these two features and derives their correlation statistically. They are then converted into soft constraints for texture transfer to accomplish adaptive multi-scale texture synthesis and to make style element distribution uniform. It allows our algorithm to produce artistic typography that fits for both local texture patterns and the global spatial distribution in the example. Experimental results demonstrate the superiority of our method for various text effects over conventional style transfer methods. In addition, we validate the effectiveness of our algorithm with extensive artistic typography library generation.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of typographic font from small font subset"):
  with members:
    - "auto encoder guided gan for chinese calligraphy synthesis"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "controllable artistic text style transfer via shape matching gan"):
  with members:
    - "context aware text based binary image stylization and synthesis"
    - "adjustable real time style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "tet gan text effects transfer via stylization and destylization"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-5 (induced by source paper "te141k artistic text benchmark for text effects transfer"):
  with members:
    - "context aware text based binary image stylization and synthesis"
    - "demystifying neural style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "toward multimodal image to image translation"
    - "universal style transfer via feature transforms"
    - "stylebank an explicit representation for neural image style transfer"

Connected through hyperedge HE-6 (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"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-7 (induced by source paper "serif or sans visual font analytics on book covers and online advertisements"):
  with members:
    - "east an efficient and accurate scene text detector"
    - "understanding infographics through textual and visual tag prediction"
    - "advise symbolism and external knowledge for decoding advertisements"
    - "automatic understanding of image and video advertisements"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deepfont identify your font from an image"

Connected through hyperedge HE-8 (induced by source paper "tet gan text effects transfer via stylization and destylization"):
  with members:
    - "image to image translation for domain adaptation"
    - "demystifying neural style transfer"
    - "progressive growing of gans for improved quality stability and variation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unsupervised image to image translation networks"
    - "learning to write stylized chinese characters by reading a handful of examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

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)