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: "multiview rectification of folded documents"
  Abstract: Digitally unwrapping images of paper sheets is crucial for accurate document scanning and text recognition. This paper presents a method for automatically rectifying curved or folded paper sheets from a few images captured from multiple viewpoints. Prior methods either need expensive 3D scanners or model deformable surfaces using over-simplified parametric representations. In contrast, our method uses regular images and is based on general developable surface models that can represent a wide variety of paper deformations. Our main contribution is a new robust rectification method based on ridge-aware 3D reconstruction of a paper sheet and unwrapping the reconstructed surface using properties of developable surfaces via $\ell_1$ conformal mapping. We present results on several examples including book pages, folded letters and shopping receipts.

Connected through hyperedge HE-1 (induced by source paper "multistage curvilinear coordinate transform based document image dewarping using a novel quality estimator"):
  with members:
    - "a method to generate synthetically warped document image"
    - "document rectification and illumination correction using a patch based cnn"

Connected through hyperedge HE-2 (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"
    - "separating style and content for generalized style transfer"

Connected through hyperedge HE-3 (induced by source paper "document rectification and illumination correction using a patch based cnn"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

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)