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 "accuracy uncertainty and adaptability of automatic myocardial asl segmentation using deep cnn"):
  Abstract: PURPOSE: To apply deep convolutional neural networks (CNN) to the left ventricular segmentation task in myocardial arterial spin labeled (ASL) perfusion imaging. To develop methods that measure uncertainty and that adapt segmentation based on a specific false positive vs. false negative tradeoff. #R##N#METHODS: We utilized a modified UNET architecture with Monte Carlo (MC) dropout. The model was trained on data from 22 subjects and tested on data from 6 heart transplant recipients. Manual segmentation and quantitative myocardial blood flow (MBF) maps were available for comparison. We consider two global scores of segmentation uncertainty, "Dice Uncertainty" and "MC Uncertainty", which were calculated with and without the use of manual segmentation, respectively. Tversky loss function with a hyperparameter $\beta$ was used to adapt the model to a specific false positive vs. false negative tradeoff. #R##N#RESULTS: The modified UNET model achieved Dice coefficient of mean(std) = $0.91(0.04)$ on the test set. MBF measured using automatic segmentation was highly correlated to that measured using the manual segmentation ($R^2 = 0.96$). Dice Uncertainty and MC Uncertainty were in good agreement ($R^2 = 0.64$). As $\beta$ increased, the false positive rate systematically decreased and false negative rate systematically increased. #R##N#CONCLUSION: We demonstrate the feasibility of using CNN for automatic segmentation of the left ventricle in myocardial ASL data. This is a particularly challenging application because of low and inconsistent blood-myocardium contrast-to-noise ratio. We also demonstrate novel methods that measure uncertainty and adapt to a desired tradeoff between false positive and false negative rates.

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 "a probabilistic u net for segmentation of ambiguous images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning in cardiology")
  Member "automated cardiovascular magnetic resonance image analysis with fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "what uncertainties do we need in bayesian deep learning for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "medgan medical image translation using gans")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "action for better prediction")
  Member "2d 3d fully convolutional neural networks for cardiac mr segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning in cardiology")

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.CV(Computer Vision and Pattern Recognition)