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 "three dimensional virtual refocusing of fluorescence microscopy images using deep learning"):
  Abstract: We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy. Deep-Z uses deep learning to go from a two-dimensional snapshot to three-dimensional fluorescence images. The method improves imaging speed while reducing light dose, and was shown to be useful for accurate structural and functional imaging of neurons in Caenorhabditis elegans.

Includes members:
  Member "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based super resolution in coherent imaging systems")
  Member "resolution enhancement in scanning electron microscopy using deep learning":
    also connected through other hyperedges:
      (none)
  Member "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram")
  Member "deep learning enhanced mobile phone microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks")
  Member "deep learning based super resolution in coherent imaging systems":
    also connected through other hyperedges:
      (none)
  Member "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "first image then video a two stage network for spatiotemporal video denoising")
  Member "deep learning microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter")
  Member "diffusercam lensless single exposure 3d imaging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "privacy preserving action recognition using coded aperture videos")

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)