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: "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
  Abstract: Abstract   Despite the state-of-the-art performance for medical image segmentation, deep convolutional neural networks (CNNs) have rarely provided uncertainty estimations regarding their segmentation outputs, e.g., model (epistemic) and image-based (aleatoric) uncertainties. In this work, we analyze these different types of uncertainties for CNN-based 2D and 3D medical image segmentation tasks at both pixel level and structure level. We additionally propose a test-time augmentation-based aleatoric uncertainty to analyze the effect of different transformations of the input image on the segmentation output. Test-time augmentation has been previously used to improve segmentation accuracy, yet not been formulated in a consistent mathematical framework. Hence, we also propose a theoretical formulation of test-time augmentation, where a distribution of the prediction is estimated by Monte Carlo simulation with prior distributions of parameters in an image acquisition model that involves image transformations and noise. We compare and combine our proposed aleatoric uncertainty with model uncertainty. Experiments with segmentation of fetal brains and brain tumors from 2D and 3D Magnetic Resonance Images (MRI) showed that 1) the test-time augmentation-based aleatoric uncertainty provides a better uncertainty estimation than calculating the test-time dropout-based model uncertainty alone and helps to reduce overconfident incorrect predictions, and 2) our test-time augmentation outperforms a single-prediction baseline and dropout-based multiple predictions.

Connected through hyperedge HE-1 (induced by source paper "f b alpha matting"):
  with members:
    - "natural image matting via guided contextual attention"
    - "on the variance of the adaptive learning rate and beyond"
    - "weight standardization"
    - "indices matter learning to index for deep image matting"
    - "context aware image matting for simultaneous foreground and alpha estimation"
    - "disentangled image matting"
    - "alphagan generative adversarial networks for natural image matting"
    - "group normalization"

Connected through hyperedge HE-2 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "annotation cost minimization for medical image segmentation using suggestive mixed supervision fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "few shot brain segmentation from weakly labeled data with deep heteroscedastic multi task networks"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "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-4 (induced by source paper "bayesian modelling in practice using uncertainty to improve trustworthiness in medical applications"):
  with members:
    - "u2 net a bayesian u net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological oct scans"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "deep reinforcement learning for sepsis treatment"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "adam a method for stochastic optimization"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-5 (induced by source paper "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"):
  with members:
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "niftynet a deep learning platform for medical imaging"
    - "no new net"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"

Connected through hyperedge HE-6 (induced by source paper "accuracy uncertainty and adaptability of automatic myocardial asl segmentation using deep cnn"):
  with members:
    - "a probabilistic u net for segmentation of ambiguous images"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deep bayesian active learning with image data"
    - "2d 3d fully convolutional neural networks for cardiac mr segmentation"
    - "v net fully convolutional neural networks for volumetric medical 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)