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 "one shot learning for deformable medical image registration and periodic motion tracking"):
  Abstract: Deformable image registration is a very important field of research in medical imaging. Recently multiple deep learning approaches were published in this area showing promising results. However, drawbacks of deep learning methods are the need for a large amount of training datasets and their inability to register unseen images different from the training datasets. One shot learning comes without the need of large training datasets and has already been proven to be applicable to 3D data. In this work we present an one shot registration approach for periodic motion tracking in 3D and 4D datasets. When applied to 3D dataset the algorithm calculates the inverse of a registration vector field simultaneously. For registration we employed a U-Net combined with a coarse to fine approach and a differential spatial transformer module. The algorithm was thoroughly tested with multiple 4D and 3D datasets publicly available. The results show that the presented approach is able to track periodic motion and to yield a competitive registration accuracy. Possible applications are the use as a stand-alone algorithm for 3D and 4D motion tracking or in the beginning of studies until enough datasets for a separate training phase are available.

Includes members:
  Member "continual lifelong learning with neural networks a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")
  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 "a deep learning framework for unsupervised affine and deformable image registration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "unsupervised learning for fast probabilistic diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "non rigid image registration using self supervised fully convolutional networks without training data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comparative study for non rigid image registration and rigid image registration")
  Member "weakly supervised convolutional neural networks for multimodal image registration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "voxelmorph a learning framework for deformable medical image registration")
  Member "birnet brain image registration using dual supervised fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning a probabilistic model for diffeomorphic registration")
  Member "end to end unsupervised deformable image registration with a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")

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)