human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "few shot brain segmentation from weakly labeled data with deep heteroscedastic multi task networks"):
  Abstract: In applications of supervised learning applied to medical image segmentation, the need for large amounts of labeled data typically goes unquestioned. In particular, in the case of brain anatomy segmentation, hundreds or thousands of weakly-labeled volumes are often used as training data. In this paper, we first observe that for many brain structures, a small number of training examples, (n=9), weakly labeled using Freesurfer 6.0, plus simple data augmentation, suffice as training data to achieve high performance, achieving an overall mean Dice coefficient of $0.84 \pm 0.12$ compared to Freesurfer over 28 brain structures in T1-weighted images of $\approx 4000$ 9-10 year-olds from the Adolescent Brain Cognitive Development study. We then examine two varieties of heteroscedastic network as a method for improving classification results. An existing proposal by Kendall and Gal, which uses Monte-Carlo inference to learn to predict the variance of each prediction, yields an overall mean Dice of $0.85 \pm 0.14$ and showed statistically significant improvements over 25 brain structures. Meanwhile a novel heteroscedastic network which directly learns the probability that an example has been mislabeled yielded an overall mean Dice of $0.87 \pm 0.11$ and showed statistically significant improvements over all but one of the brain structures considered. The loss function associated to this network can be interpreted as performing a form of learned label smoothing, where labels are only smoothed where they are judged to be uncertain.

Includes members:
  Member "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "f b alpha matting")
  Member "inherent brain segmentation quality control from fully convnet monte carlo sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks")
  Member "on the effect of inter observer variability for a reliable estimation of uncertainty of medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learn to estimate labels uncertainty for quality assurance")
  Member "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bridging the gap between ai and healthcare sides towards developing clinically relevant ai powered diagnosis systems")
  Member "neuronet fast and robust reproduction of multiple brain image segmentation pipelines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d whole brain segmentation using spatially localized atlas network tiles")
  Member "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation")
  Member "focal loss for dense object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

Question: Please tell me which class the source paper 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.LG(Machine Learning)