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: "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
  Abstract: Confocal laser endomicroscopy (CLE), although capable of obtaining images at cellular resolution during surgery of brain tumors in real time, creates as many non-diagnostic as diagnostic images. Non-useful images are often distorted due to relative motion between probe and brain or blood artifacts. Many images, however, simply lack diagnostic features immediately informative to the physician. Examining all the hundreds or thousands of images from a single case to discriminate diagnostic images from nondiagnostic ones can be tedious. Providing a real time diagnostic value assessment of images (fast enough to be used during the surgical acquisition process and accurate enough for the pathologist to rely on) to automatically detect diagnostic frames would streamline the analysis of images and filter useful images for the pathologist/surgeon. We sought to automatically classify images as diagnostic or non-diagnostic. AlexNet, a deep-learning architecture, was used in a 4-fold cross validation manner. Our dataset includes 16,795 images (8572 nondiagnostic and 8223 diagnostic) from 74 CLE-aided brain tumor surgery patients. The ground truth for all the images is provided by the pathologist. Average model accuracy on test data was 91% overall (90.79 % accuracy, 90.94 % sensitivity and 90.87 % specificity). To evaluate the model reliability we also performed receiver operating characteristic (ROC) analysis yielding 0.958 average for area under ROC curve (AUC). These results demonstrate that a deeply trained AlexNet network can achieve a model that reliably and quickly recognizes diagnostic CLE images.

Connected through hyperedge HE-1 (induced by source paper "deep transfer learning methods for colon cancer classification in confocal laser microscopy images"):
  with members:
    - "automatic plaque detection in ivoct pullbacks using convolutional neural networks"
    - "feasibility of colon cancer detection in confocal laser microscopy images using convolution neural networks"
    - "prospects for theranostics in neurosurgical imaging empowering confocal laser endomicroscopy diagnostics via deep learning"
    - "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images"
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract"):
  with members:
    - "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images"
    - "patch based carcinoma detection on confocal laser endomicroscopy images a cross site robustness assessment"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "fluorescence image histology pattern transformation using image style transfer"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "staingan stain style transfer for digital histological images"
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"):
  with members:
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep multi scale location aware 3d convolutional neural networks for automated detection of lacunes of presumed vascular origin"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "prospects for theranostics in neurosurgical imaging empowering confocal laser endomicroscopy diagnostics via deep learning"):
  with members:
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "rethinking the inception architecture for computer vision"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "object detectors emerge in deep scene cnns"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-6 (induced by source paper "image computing for fibre bundle endomicroscopy a review"):
  with members:
    - "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images"
    - "effective deep learning training for single image super resolution in endomicroscopy exploiting video registration based reconstruction"
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "understanding soft tissue behavior for application to microlaparoscopic surface scan"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images"):
  with members:
    - "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"
    - "learning deep features for discriminative localization"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

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)