human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "image computing for fibre bundle endomicroscopy a review"):
  Abstract: Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre- and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed.

Includes members:
  Member "weakly supervised learning based feature localization in confocal laser endomicroscopy glioma images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep transfer learning methods for colon cancer classification in confocal laser microscopy images")
  Member "effective deep learning training for single image super resolution in endomicroscopy exploiting video registration based reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial training with cycle consistency for unsupervised super resolution in endomicroscopy")
  Member "convolutional neural networks ensemble modeling fine tuning and unsupervised semantic localization for neurosurgical cle images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tkd temporal knowledge distillation for active perception")
  Member "convolutional neural networks for medical image analysis full training or fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of digital health with federated learning")
  Member "improving utility of brain tumor confocal laser endomicroscopy objective value assessment and diagnostic frame detection with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep transfer learning methods for colon cancer classification in confocal laser microscopy images")
  Member "automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep transfer learning methods for colon cancer classification in confocal laser microscopy images")
  Member "rethinking the inception architecture for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

Question: Please tell me which class the source paper 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)