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: "building medical image classifiers with very limited data using segmentation networks"
  Abstract: Abstract   Deep learning has shown promising results in medical image analysis, however, the lack of very large annotated datasets confines its full potential. Although transfer learning with ImageNet pre-trained classification models can alleviate the problem, constrained image sizes and model complexities can lead to unnecessary increase in computational cost and decrease in performance. As many common morphological features are usually shared by different classification tasks of an organ, it is greatly beneficial if we can extract such features to improve classification with limited samples. Therefore, inspired by the idea of curriculum learning, we propose a strategy for building medical image classifiers using features from segmentation networks. By using a segmentation network pre-trained on similar data as the classification task, the machine can first learn the simpler shape and structural concepts before tackling the actual classification problem which usually involves more complicated concepts. Using our proposed framework on a 3D three-class brain tumor type classification problem, we achieved 82% accuracy on 191 testing samples with 91 training samples. When applying to a 2D nine-class cardiac semantic level classification problem, we achieved 86% accuracy on 263 testing samples with 108 training samples. Comparisons with ImageNet pre-trained classifiers and classifiers trained from scratch are presented.

Connected through hyperedge HE-1 (induced by source paper "cross modality ct mri prior augmented deep learning for robust lung tumor segmentation from small mr datasets"):
  with members:
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"
    - "distribution matching losses can hallucinate features in medical image translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "adversarial synthesis learning enables segmentation without target modality ground truth"
    - "deep mr to ct synthesis using unpaired data"
    - "learning from simulated and unsupervised images through adversarial training"

Connected through hyperedge HE-2 (induced by source paper "learning to segment images with classification labels"):
  with members:
    - "convolutional sparse kernel network for unsupervised medical image analysis"
    - "multimodal self supervised learning for medical image analysis"
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "unsupervised learning for cell level visual representation in histopathology images with generative adversarial networks"
    - "a survey of crowdsourcing in medical image analysis"
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"
    - "unsupervised representation learning by predicting image rotations"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"

Connected through hyperedge HE-3 (induced by source paper "cats or cat scans transfer learning from natural or medical image source data sets"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "is the winner really the best a critical analysis of common research practice in biomedical image analysis competitions"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "knowledge transfer for melanoma screening with deep learning"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "imagenet large scale visual recognition challenge"

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)