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: "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
  Abstract: The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtual staining method and standard histological staining using microscopic images of human tissue sections of the salivary gland, thyroid, kidney, liver and lung, and involving different types of stain, showed no major discordances. The virtual-staining method bypasses the typically labour-intensive and costly histological staining procedures, and could be used as a blueprint for the virtual staining of tissue images acquired with other label-free imaging modalities. Deep learning can be used to virtually stain autofluorescence images of unlabelled tissue sections, generating images that are equivalent to the histologically stained versions.

Connected through hyperedge HE-1 (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"
    - "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"
    - "deep learning microscopy"

Connected through hyperedge HE-2 (induced by source paper "gan based virtual re staining a promising solution for whole slide image analysis"):
  with members:
    - "deep photo style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning from simulated and unsupervised images through adversarial training"
    - "image to image translation with conditional adversarial networks"
    - "deep residual learning for image recognition"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-3 (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:
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "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-4 (induced by source paper "perceptual embedding consistency for seamless reconstruction of tilewise style transfer"):
  with members:
    - "computational histological staining and destaining of prostate core biopsy rgb images with generative adversarial neural networks"
    - "staingan stain style transfer for digital histological images"
    - "virtualization of tissue staining in digital pathology using an unsupervised deep learning approach"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "fluorescence image histology pattern transformation using image style transfer"):
  with members:
    - "staingan stain style transfer for digital histological images"
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"

Connected through hyperedge HE-6 (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"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "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-7 (induced by source paper "virtualization of tissue staining in digital pathology using an unsupervised deep learning approach"):
  with members:
    - "generalizing multistain immunohistochemistry tissue segmentation using one shot color deconvolution deep neural networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "revisiting distributed synchronous sgd"
    - "instance normalization the missing ingredient for fast stylization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "toward a thinking microscope"):
  with members:
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "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)