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: "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images"
  Abstract: Abstract   Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence technology undergoing assessment for applications in brain tumor surgery. Many of the CLE images can be distorted and interpreted as nondiagnostic. However, just one neat CLE image might suffice for intraoperative diagnosis of the tumor. While manual examination of thousands of nondiagnostic images during surgery would be impractical, this creates an opportunity for a model to select diagnostic images for the pathologists or surgeons review. In this study, we sought to develop a deep learning model to automatically detect the diagnostic images. We explored the effect of training regimes and ensemble modeling and localized histological features from diagnostic CLE images. The developed model could achieve promising agreement with the ground truth. With the speed and precision of the proposed method, it has potential to be integrated into the operative workflow in the brain tumor surgery.

Connected through hyperedge HE-1 (induced by source paper "tkd temporal knowledge distillation for active perception"):
  with members:
    - "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"
    - "real world anomaly detection in surveillance videos"
    - "object detection at 200 frames per second"
    - "yolov3 an incremental improvement"
    - "gaple generalizable approaching policy learning for robotic object searching in indoor environment"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "online model distillation for efficient video inference"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"

Connected through hyperedge HE-2 (induced by source paper "a novel design of adaptive and hierarchical convolutional neural networks using partial reconfiguration on fpga"):
  with members:
    - "skipnet learning dynamic routing in convolutional networks"
    - "adaptive neural networks for efficient inference"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "adaptive feeding achieving fast and accurate detections by adaptively combining object detectors"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "learning structured sparsity in deep neural networks"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (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 for medical image analysis full training or fine tuning"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning"
    - "squeeze and excitation networks"

Connected through hyperedge HE-4 (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"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"

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 for medical image analysis full training or fine tuning"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "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 for medical image analysis full training or fine tuning"
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "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:
    - "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks"
    - "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)