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: "spatial and angular resolution enhancement of light fields using convolutional neural networks"
  Abstract: Light field imaging extends the traditional photography by capturing both spatial and angular distribution of light, which enables new capabilities, including post-capture refocusing, post-capture aperture control, and depth estimation from a single shot. Micro-lens array (MLA) based light field cameras offer a cost-effective approach to capture light field. A major drawback of MLA based light field cameras is low spatial resolution, which is due to the fact that a single image sensor is shared to capture both spatial and angular information. In this paper, we present a learning based light field enhancement approach. Both spatial and angular resolution of captured light field is enhanced using convolutional neural networks. The proposed method is tested with real light field data captured with a Lytro light field camera, clearly demonstrating spatial and angular resolution improvement.

Connected through hyperedge HE-1 (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"
    - "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"
    - "partial light field tomographic reconstruction from a fixed camera focal stack"

Connected through hyperedge HE-2 (induced by source paper "a simple framework to leverage state of the art single image super resolution methods to restore light fields"):
  with members:
    - "geometry consistent light field super resolution via graph based regularization"
    - "enhanced deep residual networks for single image super resolution"
    - "light field video capture using a learning based hybrid imaging system"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "geometry aware neighborhood search for learning local models for image superresolution"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "accurate image super resolution using very deep convolutional networks"

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)