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: "separating style and content for generalized style transfer"
  Abstract: Neural style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here attempt to separate the representations for styles and contents, and propose a generalized style transfer network consisting of style encoder, content encoder, mixer and decoder. The style encoder and content encoder are used to extract the style and content factors from the style reference images and content reference images, respectively. The mixer employs a bilinear model to integrate the above two factors and finally feeds it into a decoder to generate images with target style and content. To separate the style features and content features, we leverage the conditional dependence of styles and contents given an image. During training, the encoder network learns to extract styles and contents from two sets of reference images in limited size, one with shared style and the other with shared content. This learning framework allows simultaneous style transfer among multiple styles and can be deemed as a special `multi-task' learning scenario. The encoders are expected to capture the underlying features for different styles and contents which is generalizable to new styles and contents. For validation, we applied the proposed algorithm to the Chinese Typeface transfer problem. Extensive experiment results on character generation have demonstrated the effectiveness and robustness of our method.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "attention aware multi stroke style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "difnet semantic segmentation by diffusion networks"
    - "style separation and synthesis via generative adversarial networks"
    - "deep back projection networks for super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"

Connected through hyperedge HE-4 (induced by source paper "dual adversarial inference for text to image synthesis"):
  with members:
    - "mirrorgan learning text to image generation by redescription"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "adversarial learning of semantic relevance in text to image synthesis"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "multimodal style transfer via graph cuts"):
  with members:
    - "rethinking zero shot learning a conditional visual classification perspective"
    - "visual semantic reasoning for image text matching"
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the contextual loss for image transformation with non aligned data"
    - "demystifying neural style transfer"

Connected through hyperedge HE-6 (induced by source paper "large scale tag based font retrieval with generative feature learning"):
  with members:
    - "stacked cross attention for image text matching"
    - "identity aware textual visual matching with latent co attention"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "person search with natural language description"
    - "dual path convolutional image text embedding with instance loss"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to compare relation network for few shot learning"
    - "multi content gan for few shot font style transfer"

Connected through hyperedge HE-7 (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"
    - "auto encoder guided gan for chinese calligraphy synthesis"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "fontgan a unified generative framework for chinese character stylization and de stylization"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "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"
    - "generative adversarial text to image synthesis"

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)