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: "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.

Connected through hyperedge HE-1 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images"):
  with members:
    - "self supervised learning for cardiac mr image segmentation by anatomical position prediction"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-2 (induced by source paper "automatic detection of rare pathologies in fundus photographs using few shot learning"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a survey on deep learning in medical image analysis"
    - "attentive recurrent comparators"

Connected through hyperedge HE-3 (induced by source paper "automatic detection of multiple pathologies in fundus photographs using spin off learning"):
  with members:
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "a survey on deep learning in medical image analysis"
    - "attentive recurrent comparators"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

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)