human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a simple framework to leverage state of the art single image super resolution methods to restore light fields"):
  Abstract: Plenoptic cameras offer a cost effective solution to capture light fields by multiplexing multiple views on a single image sensor. However, the high angular resolution is achieved at the expense of reducing the spatial resolution of each view by orders of magnitude compared to the raw sensor image. While light field super-resolution is still at an early stage, the field of single image super-resolution (SISR) has recently known significant advances with the use of deep learning techniques. This paper describes a simple framework allowing us to leverage state-of-the-art SISR techniques into light fields, while taking into account specific light field geometrical constraints. The idea is to first compute a representation compacting most of the light field energy into as few components as possible. This is achieved by aligning the light field using optical flows and then by decomposing the aligned light field using singular value decomposition (SVD). The principal basis captures the information that is coherent across all the views, while the other basis contain the high angular frequencies. Super-resolving this principal basis using an SISR method allows us to super-resolve all the information that is coherent across the entire light field. This framework allows the proposed light field super-resolution method to inherit the benefits of the SISR method used. Experimental results show that the proposed method is competitive, and most of the time superior, to recent light field super-resolution methods in terms of both PSNR and SSIM quality metrics, with a lower complexity.

Includes members:
  Member "spatial and angular resolution enhancement of light fields using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a unified learning based framework for light field reconstruction from coded projections")
  Member "geometry consistent light field super resolution via graph based regularization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning high fidelity light field images from hybrid inputs")
  Member "enhanced deep residual networks for single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "light field video capture using a learning based hybrid imaging system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography")
  Member "beyond deep residual learning for image restoration persistent homology guided manifold simplification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi level wavelet convolutional neural networks")
  Member "geometry aware neighborhood search for learning local models for image superresolution":
    also connected through other hyperedges:
      (none)
  Member "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "accurate image super resolution using very deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")

Question: Please tell me which class the source paper 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)