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: "partial light field tomographic reconstruction from a fixed camera focal stack"
  Abstract: This paper describes a novel approach to partially reconstruct high-resolution 4D light fields from a stack of differently focused photographs taken with a fixed camera. First, a focus map is calculated from this stack using a simple approach combining gradient detection and region expansion with graph-cut. Then, this focus map is converted into a depth map thanks to the calibration of the camera. We proceed after this with the tomographic reconstruction of the epipolar images by back-projecting the focused regions of the scene only. We call it masked back-projection. The angles of back-projection are calculated from the depth map. Thanks to the high angular resolution we achieve by suitably exploiting the image content captured over a large interval of focus distances, we are able to render puzzling perspective shifts although the original photographs were taken from a single fixed camera at a fixed position.

Connected through hyperedge HE-1 (induced by source paper "mffw a new dataset for multi focus image fusion"):
  with members:
    - "an α matte boundary defocus model based cascaded network for multi focus image fusion"
    - "sesf fuse an unsupervised deep model for multi focus image fusion"
    - "a multiple source hourglass deep network for multi focus image fusion"
    - "boundary aware multi focus image fusion using deep neural network"

Connected through hyperedge HE-2 (induced by source paper "a fourier disparity layer representation for light fields"):
  with members:
    - "learning based view synthesis for light field cameras"
    - "light field reconstruction using shearlet transform"

Connected through hyperedge HE-3 (induced by source paper "a unified learning based framework for light field reconstruction from coded projections"):
  with members:
    - "learning light field reconstruction from a single coded image"
    - "spatial and angular resolution enhancement of light fields using convolutional neural networks"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "aperture supervision for monocular depth estimation"
    - "unsupervised monocular depth estimation with left right consistency"
    - "learning based view synthesis for light field cameras"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

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)