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: "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
  Abstract: Deep learning (DL) networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets [3,11,16], especially for large pathologies. However, in the context of diseases such as Multiple Sclerosis (MS), monitoring all the focal lesions visible on MRI sequences, even very small ones, is essential for disease staging, prognosis, and evaluating treatment efficacy. Moreover, producing deterministic outputs hinders DL adoption into clinical routines. Uncertainty estimates for the predictions would permit subsequent revision by clinicians. We present the first exploration of multiple uncertainty estimates based on Monte Carlo (MC) dropout [4] in the context of deep networks for lesion detection and segmentation in medical images. Specifically, we develop a 3D MS lesion segmentation CNN, augmented to provide four different voxel-based uncertainty measures based on MC dropout. We train the network on a proprietary, large-scale, multi-site, multi-scanner, clinical MS dataset, and compute lesion-wise uncertainties by accumulating evidence from voxel-wise uncertainties within detected lesions. We analyze the performance of voxel-based segmentation and lesion-level detection by choosing operating points based on the uncertainty. Empirical evidence suggests that uncertainty measures consistently allow us to choose superior operating points compared only using the network's sigmoid output as a probability.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems"):
  with members:
    - "learning more with less conditional pggan based data augmentation for brain metastases detection using highly rough annotation on mr images"
    - "enforcing constraints for interpolation and extrapolation in generative adversarial networks"
    - "learning more with less gan based medical image augmentation"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "coco gan generation by parts via conditional coordinating"
    - "lung nodule classification using deep local global networks"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "adaptive weighting multi field of view cnn for semantic segmentation in pathology"

Connected through hyperedge HE-2 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-3 (induced by source paper "unsupervised data uncertainty learning in visual retrieval systems"):
  with members:
    - "exploring uncertainty in conditional multi modal retrieval systems"
    - "features for multi target multi camera tracking and re identification"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "in defense of the triplet loss for person re identification"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "deep bayesian active learning with image data"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "pedestrian alignment network for large scale person re identification"

Connected through hyperedge HE-4 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "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-5 (induced by source paper "on modelling label uncertainty in deep neural networks automatic estimation of intra observer variability in 2d echocardiography quality assessment"):
  with members:
    - "a machine learning framework for automatic reference free quality assessment in mri"
    - "bayesian quicknat model uncertainty in deep whole brain segmentation for structure wise quality control"
    - "on calibration of modern neural networks"
    - "deep label distribution learning with label ambiguity"
    - "comparative study of cnn and rnn for natural language processing"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "uncertainty guided domain alignment for layer segmentation in oct images"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "joint segmentation and uncertainty visualization of retinal layers in optical coherence tomography images using bayesian deep learning"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "adversarial domain adaptation for classification of prostate histopathology whole slide images"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"

Connected through hyperedge HE-7 (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"
    - "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"
    - "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-8 (induced by source paper "bayesian modelling in practice using uncertainty to improve trustworthiness in medical applications"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "u2 net a bayesian u net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological oct scans"
    - "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"

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)