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: "error corrective boosting for learning fully convolutional networks with limited data"
  Abstract: Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 secs in comparison to 30 hours for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.

Connected through hyperedge HE-1 (induced by source paper "fatsegnet a fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon mri"):
  with members:
    - "fully convolutional networks for automated segmentation of abdominal adipose tissue depots in multicenter water fat mri"
    - "competition vs concatenation in skip connections of fully convolutional networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "data augmentation using learned transformations for one shot medical image segmentation"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "nonparametric density flows for mri intensity normalisation"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "few shot segmentation propagation with guided networks"
    - "autoaugment learning augmentation policies from data"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "one shot learning for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "feature fused context encoding network for neuroanatomy segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "context encoding for semantic segmentation"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "mask r cnn"
    - "densely connected convolutional networks"
    - "pyramid scene parsing network"
    - "automatic segmentation of mr brain images with a convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "3d whole brain segmentation using spatially localized atlas network tiles"):
  with members:
    - "compnet complementary segmentation network for brain mri extraction"
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "4d multi atlas label fusion using longitudinal images"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-5 (induced by source paper "3dq compact quantized neural networks for volumetric whole brain segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "defensive quantization when efficiency meets robustness"
    - "federated learning strategies for improving communication efficiency"
    - "ternarynet faster deep model inference without gpus for medical 3d segmentation using sparse and binary convolutions"
    - "a survey of model compression and acceleration for deep neural networks"
    - "ternary weight networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "exploit fully automatic low level segmented pet data for training high level deep learning algorithms for the corresponding ct data"):
  with members:
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "understanding data augmentation for classification when to warp"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "reproducibility evaluation of slant whole brain segmentation across clinical magnetic resonance imaging protocols"):
  with members:
    - "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "deep neural networks for anatomical brain segmentation"

Connected through hyperedge HE-8 (induced by source paper "building medical image classifiers with very limited data using segmentation networks"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "building disease detection algorithms with very small numbers of positive samples"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "context encoders feature learning by inpainting"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning in neural networks"

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)