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 "3d whole brain segmentation using spatially localized atlas network tiles"):
  Abstract: Detailed whole brain segmentation is an essential quantitative technique, which provides a non-invasive way of measuring brain regions from a structural magnetic resonance imaging (MRI). Recently, deep convolution neural network (CNN) has been applied to whole brain segmentation. However, restricted by current GPU memory, 2D based methods, downsampling based 3D CNN methods, and patch-based high-resolution 3D CNN methods have been the de facto standard solutions. 3D patch-based high resolution methods typically yield superior performance among CNN approaches on detailed whole brain segmentation (>100 labels), however, whose performance are still commonly inferior compared with multi-atlas segmentation methods (MAS) due to the following challenges: (1) a single network is typically used to learn both spatial and contextual information for the patches, (2) limited manually traced whole brain volumes are available (typically less than 50) for training a network. In this work, we propose the spatially localized atlas network tiles (SLANT) method to distribute multiple independent 3D fully convolutional networks (FCN) for high-resolution whole brain segmentation. To address the first challenge, multiple spatially distributed networks were used in the SLANT method, in which each network learned contextual information for a fixed spatial location. To address the second challenge, auxiliary labels on 5111 initially unlabeled scans were created by multi-atlas segmentation for training. Since the method integrated multiple traditional medical image processing methods with deep learning, we developed a containerized pipeline to deploy the end-to-end solution. From the results, the proposed method achieved superior performance compared with multi-atlas segmentation methods, while reducing the computational time from >30 hours to 15 minutes (this https URL).

Includes members:
  Member "compnet complementary segmentation network for brain mri extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation")
  Member "spatially localized atlas network tiles enables 3d whole brain segmentation from limited data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic atlases to enforce topological constraints")
  Member "neuronet fast and robust reproduction of multiple brain image segmentation pipelines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot brain segmentation from weakly labeled data with deep heteroscedastic multi task networks")
  Member "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for automated medical image analysis")
  Member "4d multi atlas label fusion using longitudinal images":
    also connected through other hyperedges:
      (none)
  Member "error corrective boosting for learning fully convolutional networks with limited data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fatsegnet a fully automated deep learning pipeline for adipose tissue segmentation on abdominal dixon mri")
  Member "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mri super resolution with gan and 3d multi level densenet smaller faster and better")
  Member "deepnat deep convolutional neural network for segmenting neuroanatomy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review")

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)