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 labeled atlases are necessary for deep learning based segmentation"):
  Abstract: We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images. This is an important and challenging task in medical applications, where manual annotations are time-consuming. Classical multi-atlas based anatomical segmentation methods use image registration to warp segments from labeled images onto a new scan. These approaches have traditionally required significant runtime, but recent learning-based registration methods promise substantial runtime improvement. In a different paradigm, supervised learning-based segmentation strategies have gained popularity. These methods have consistently used relatively large sets of labeled training data, and their behavior in the regime of a few labeled images has not been thoroughly evaluated. In this work, we provide two important results for anatomical segmentation in the scenario where few labeled images are available. First, we propose a straightforward implementation of efficient semi-supervised learning-based registration method, which we showcase in a multi-atlas segmentation framework. Second, through a thorough empirical study, we evaluate the performance of a supervised segmentation approach, where the training images are augmented via random deformations. Surprisingly, we find that in both paradigms, accurate segmentation is generally possible even in the context of few labeled images.

Includes members:
  Member "data augmentation using learned transformations for one shot medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lt net label transfer by learning reversible voxel wise correspondence for one shot medical image segmentation")
  Member "learning a probabilistic model for diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation")
  Member "semi supervised and task driven data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised deep learning for bayesian brain mri segmentation")
  Member "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "volumetric landmark detection with a multi scale shift equivariant neural network")
  Member "voxelmorph a learning framework for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "a deep learning framework for unsupervised affine and deformable image registration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "weakly supervised convolutional neural networks for multimodal image registration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "voxelmorph a learning framework for deformable medical image registration")
  Member "anatomical priors in convolutional networks for unsupervised biomedical segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation")

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)