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: "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images"
  Abstract: Confocal Laser Endomicroscope (CLE) is a novel handheld fluorescence imaging device that has shown promise for rapid intraoperative diagnosis of brain tumor tissue. Currently CLE is capable of image display only and lacks an automatic system to aid the surgeon in analyzing the images. The goal of this project was to develop a computer-aided diagnostic approach for CLE imaging of human glioma with feature localization function. Despite the tremendous progress in object detection and image segmentation methods in recent years, most of such methods require large annotated datasets for training. However, manual annotation of thousands of histopathological images by physicians is costly and time consuming. To overcome this problem, we propose a Weakly-Supervised Learning (WSL)-based model for feature localization that trains on image-level annotations, and then localizes incidences of a class-of-interest in the test image. We developed a novel convolutional neural network for diagnostic features localization from CLE images by employing a novel multiscale activation map that is laterally inhibited and collaterally integrated. To validate our method, we compared proposed model's output to the manual annotation performed by four neurosurgeons on test images. Proposed model achieved 88% mean accuracy and 86% mean intersection over union on intermediate features and 87% mean accuracy and 88% mean intersection over union on restrictive fine features, while outperforming other state of the art methods tested. This system can improve accuracy and efficiency in characterization of CLE images of glioma tissue during surgery, augment intraoperative decision-making process regarding tumor margin and affect resection rates.

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"
    - "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"
    - "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-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:
    - "patch based carcinoma detection on confocal laser endomicroscopy images a cross site robustness assessment"
    - "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"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "image computing for fibre bundle endomicroscopy a review"):
  with members:
    - "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"
    - "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"

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)