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: "photo realistic facial texture transfer"
  Abstract: Style transfer methods have achieved significant success in recent years with the use of convolutional neural networks. However, many of these methods concentrate on artistic style transfer with few constraints on the output image appearance. We address the challenging problem of transferring face texture from a style face image to a content face image in a photorealistic manner without changing the identity of the original content image. Our framework for face texture transfer (FaceTex) augments the prior work of MRF-CNN with a novel facial semantic regularization that incorporates a face prior regularization smoothly suppressing the changes around facial meso-structures (e.g eyes, nose and mouth) and a facial structure loss function which implicitly preserves the facial structure so that face texture can be transferred without changing the original identity. We demonstrate results on face images and compare our approach with recent state-of-the-art methods. Our results demonstrate superior texture transfer because of the ability to maintain the identity of the original face image.

Connected through hyperedge HE-1 (induced by source paper "dual reference age synthesis"):
  with members:
    - "face aging with contextual generative adversarial nets"
    - "face aging with conditional generative adversarial networks"
    - "exprgan facial expression editing with controllable expression intensity"
    - "learning face age progression a pyramid architecture of gans"
    - "age progression regression by conditional adversarial autoencoder"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "fader networks manipulating images by sliding attributes"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "semi supervised learning for face sketch synthesis in the wild"):
  with members:
    - "random sampling and locality constraint for face sketch"
    - "random sampling for fast face sketch synthesis"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "towards realistic face photo sketch synthesis via composition aided gans"
    - "content adaptive sketch portrait generation by decompositional representation learning"
    - "perceptual losses for real time style transfer and super resolution"
    - "combining markov random fields and convolutional neural networks for image synthesis"

Connected through hyperedge HE-3 (induced by source paper "deepdeblur fast one step blurry face images restoration"):
  with members:
    - "learning from simulated and unsupervised images through adversarial training"
    - "a neural approach to blind motion deblurring"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "going deeper with convolutions"
    - "learning to deblur"

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)