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 "cats or cat scans transfer learning from natural or medical image source data sets"):
  Abstract: Abstract   Transfer learning is a widely used strategy in medical image analysis. Instead of only training a network with a limited amount of data from the target task of interest, we can first train the network with other, potentially larger source data sets, creating a more robust model. The source data sets do not have to be related to the target task. For a classification task in lung computed tomography (CT) images, we could use both head CT images and images of cats as the source. While head CT images appear more similar to lung CT images, the number and diversity of cat images might lead to a better model overall. In this survey, we review a number of articles that have studied similar comparisons. Although the answer to which strategy is best seems to be ‘it depends’, we discuss a number of research directions we need to take as a community to gain more understanding of this topic.

Includes members:
  Member "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on semi self and unsupervised techniques in image classification")
  Member "is the winner really the best a critical analysis of common research practice in biomedical image analysis competitions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting breast tumor proliferation from whole slide images the tupac16 challenge")
  Member "building medical image classifiers with very limited data using segmentation networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross modality ct mri prior augmented deep learning for robust lung tumor segmentation from small mr datasets")
  Member "multisource transfer learning with convolutional neural networks for lung pattern analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "convolutional neural networks for medical image analysis full training or fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the future of digital health with federated learning")
  Member "knowledge transfer for melanoma screening with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  Member "a survey on deep learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "medgan medical image translation using gans")

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)