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: "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
  Abstract: Holography encodes the three-dimensional (3D) information of a sample in the form of an intensity-only recording. However, to decode the original sample image from its hologram(s), autofocusing and phase recovery are needed, which are in general cumbersome and time-consuming to perform digitally. Here we demonstrate a convolutional neural network (CNN)-based approach that simultaneously performs autofocusing and phase recovery to significantly extend the depth of field (DOF) and the reconstruction speed in holographic imaging. For this, a CNN is trained by using pairs of randomly defocused back-propagated holograms and their corresponding in-focus phase-recovered images. After this training phase, the CNN takes a single back-propagated hologram of a 3D sample as input to rapidly achieve phase recovery and reconstruct an in-focus image of the sample over a significantly extended DOF. This deep-learning-based DOF extension method is non-iterative and significantly improves the algorithm time complexity of holographic image reconstruction from O(nm) to O(1), where n refers to the number of individual object points or particles within the sample volume, and m represents the focusing search space within which each object point or particle needs to be individually focused. These results highlight some of the unique opportunities created by data-enabled statistical image reconstruction methods powered by machine learning, and we believe that the presented approach can be broadly applicable to computationally extend the DOF of other imaging modalities.

Connected through hyperedge HE-1 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning"):
  with members:
    - "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram"
    - "resolution enhancement in scanning electron microscopy using deep learning"
    - "deep learning enhanced mobile phone microscopy"
    - "deep learning based super resolution in coherent imaging systems"
    - "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks"
    - "deep learning microscopy"
    - "diffusercam lensless single exposure 3d imaging"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through 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"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "lensless computational imaging through deep learning"
    - "image to image translation with conditional adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "quantitative phase imaging and artificial intelligence a review"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "choicenet robust learning by revealing output correlations"
    - "deep learning enhanced mobile phone microscopy"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "deep learning based super resolution in coherent imaging systems"):
  with members:
    - "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "toward a thinking microscope"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "lensless computational imaging through deep learning"

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)