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 "closing the gap between deep and conventional image registration using probabilistic dense displacement networks"):
  Abstract: Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion analysis all rely heavily on accurate intra-patient alignment. Furthermore, inter-patient registration enables atlas-based segmentation or landmark localisation and shape analysis. When labelled scans are scarce and anatomical differences large, conventional registration has often remained superior to deep learning methods that have so far mainly dealt with relatively small or low-complexity deformations. We address this shortcoming by leveraging ideas from probabilistic dense displacement optimisation that has excelled in many registration tasks with large deformations. We propose to design a network with approximate min-convolutions and mean field inference for differentiable displacement regularisation within a discrete weakly-supervised registration setting. By employing these meaningful and theoretically proven constraints, our learnable registration algorithm contains very few trainable weights (primarily for feature extraction) and is easier to train with few labelled scans. It is very fast in training and inference and achieves state-of-the-art accuracies for the challenging inter-patient registration of abdominal CT outperforming previous deep learning approaches by 15% Dice overlap.

Includes members:
  Member "voxelmorph a learning framework for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "voxelmorph a learning framework for deformable medical image registration")
  Member "an unsupervised learning model for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "unsupervised probabilistic deformation modeling for robust diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised 3d end to end medical image registration with volume tweening network")
  Member "estimation of large motion in lung ct by integrating regularized keypoint correspondences into dense deformable registration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mlvirnet multilevel variational image registration network")
  Member "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "medgan medical image translation using gans")
  Member "quicksilver fast predictive image registration a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)