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 "ophthalmic diagnosis using deep learning with fundus images a critical review"):
  Abstract: Abstract   An overview of the applications of deep learning for ophthalmic diagnosis using retinal fundus images is presented. We describe various retinal image datasets that can be used for deep learning purposes. Applications of deep learning for segmentation of optic disk,optic cup, blood vessels as well as detection of lesions are reviewed. Recent deep learning models for classification of diseases such as age-related macular degeneration, glaucoma and diabetic retinopathy are also discussed. Important critical insights and future research directions have been given.

Includes members:
  Member "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning in cardiology")
  Member "embedded deep learning in ophthalmology making ophthalmic imaging smarter":
    also connected through other hyperedges:
      (none)
  Member "classification of findings with localized lesions in fundoscopic images using a regionally guided cnn":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "deep supervision with additional labels for retinal vessel segmentation task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph attention network based pruning for reconstructing 3d liver vessel morphology from contrasted ct images")
  Member "deep visual domain adaptation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "predicting optical coherence tomography derived diabetic macular edema grades from fundus photographs using deep learning":
    also connected through other hyperedges:
      (none)
  Member "deep learning based retinal oct segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards ophthalmologist level accurate deep learning system for oct screening and diagnosis")
  Member "disc aware ensemble network for glaucoma screening from fundus image":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "patch based output space adversarial learning for joint optic disc and cup segmentation")

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)