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: "deep learning microscopy"
  Abstract: We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field of view and depth of field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with better resolution, matching the performance of higher numerical aperture lenses and also significantly surpassing their limited field of view and depth of field. These results are significant for various fields that use microscopy tools, including, e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, the presented approach might be applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better as they continue to image specimens and establish new transformations among different modes of imaging.

Connected through hyperedge HE-1 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "the intelligent icu pilot study using artificial intelligence technology for autonomous patient monitoring"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "an unsupervised learning model for deformable medical image registration"
    - "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"

Connected through hyperedge HE-2 (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"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "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"
    - "diffusercam lensless single exposure 3d imaging"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-3 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "class specific differential detection in diffractive optical neural networks improves inference accuracy"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "all optical machine learning using diffractive deep neural networks"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "principles of neuromorphic photonics"
    - "lensless computational imaging through deep learning"

Connected through hyperedge HE-4 (induced by source paper "does deep learning always outperform simple linear regression in optical imaging"):
  with members:
    - "review on optical image hiding and watermarking techniques"
    - "experimental comparison of single pixel imaging algorithms"
    - "end to end deep learning of optical fiber communications"
    - "deep learning approach for fourier ptychography microscopy"
    - "computational ghost imaging using deep learning"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "solving fourier ptychographic imaging problems via neural network modeling and tensorflow"
    - "lensless computational imaging through deep learning"

Connected through hyperedge HE-5 (induced by source paper "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "identity mappings in deep residual networks"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "blind deconvolution microscopy using cycle consistent cnn with explicit psf layer"):
  with members:
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "attribute guided face generation using conditional cyclegan"
    - "instance normalization the missing ingredient for fast stylization"
    - "least squares generative adversarial networks"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning a deep convolution network with turing test adversaries for microscopy image super resolution"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "deep learning enhanced mobile phone microscopy"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "resolution enhancement in scanning electron microscopy using deep learning"):
  with members:
    - "bayesian deconvolution of scanning electron microscopy images using point spread function estimation and non local regularization"
    - "deep learning enhanced mobile phone microscopy"
    - "sparsity based super resolution for sem images"
    - "multi resolution data fusion for super resolution electron microscopy"
    - "u net convolutional networks for biomedical image segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "image super resolution using 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.LG(Machine Learning)