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: "inherent brain segmentation quality control from fully convnet monte carlo sampling"
  Abstract: We introduce inherent measures for effective quality control of brain segmentation based on a Bayesian fully convolutional neural network, using model uncertainty. Monte Carlo samples from the posterior distribution are efficiently generated using dropout at test time. Based on these samples, we introduce next to a voxel-wise uncertainty map also three metrics for structure-wise uncertainty. We then incorporate these structure-wise uncertainty in group analyses as a measure of confidence in the observation. Our results show that the metrics are highly correlated to segmentation accuracy and therefore present an inherent measure of segmentation quality. Furthermore, group analysis with uncertainty results in effect sizes closer to that of manual annotations. The introduced uncertainty metrics can not only be very useful in translation to clinical practice but also provide automated quality control and group analyses in processing large data repositories.

Connected through hyperedge HE-1 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"):
  with members:
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "data distillation towards omni supervised learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"

Connected through hyperedge HE-2 (induced by source paper "fast bayesian uncertainty estimation of batch normalized single image super resolution network"):
  with members:
    - "deep learning for image super resolution a survey"
    - "learning to decode 7t like mr image reconstruction from 3t mr images"
    - "residual dense network for image super resolution"
    - "densely connected pyramid dehazing network"
    - "fully convolutional model for variable bit length and lossy high density compression of mammograms"
    - "sketch based manga retrieval using manga109 dataset"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "a sparse annotation strategy based on attention guided active learning for 3d medical image segmentation"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "dual attention network for scene segmentation"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "few shot brain segmentation from weakly labeled data with deep heteroscedastic multi task networks"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "on the effect of inter observer variability for a reliable estimation of uncertainty of medical image segmentation"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "neuronet fast and robust reproduction of multiple brain image segmentation pipelines"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "focal loss for dense object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-5 (induced by source paper "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "on calibration of modern neural networks"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"
    - "densely connected convolutional networks"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "u net convolutional networks for biomedical image segmentation"

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)