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 "networks for joint affine and non parametric image registration"):
  Abstract: We introduce an end-to-end deep-learning framework for 3D medical image registration. In contrast to existing approaches, our framework combines two registration methods: an affine registration and a vector momentum-parameterized stationary velocity field (vSVF) model. Specifically, it consists of three stages. In the first stage, a multi-step affine network predicts affine transform parameters. In the second stage, we use a Unet-like network to generate a momentum, from which a velocity field can be computed via smoothing. Finally, in the third stage, we employ a self-iterable map-based vSVF component to provide a non-parametric refinement based on the current estimate of the transformation map. Once the model is trained, a registration is completed in one forward pass. To evaluate the performance, we conducted longitudinal and cross-subject experiments on 3D magnetic resonance images (MRI) of the knee of the Osteoarthritis Initiative (OAI) dataset. Results show that our framework achieves comparable performance to state-of-the-art medical image registration approaches, but it is much faster, with a better control of transformation regularity including the ability to produce approximately symmetric transformations, and combining affine and non-parametric registration.

Includes members:
  Member "unsupervised learning for fast probabilistic diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "airnet self supervised affine registration for 3d medical images using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neurreg neural registration and its application to image segmentation")
  Member "an unsupervised learning model for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "non rigid image registration using fully convolutional networks with deep self supervision":
    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 "end to end unsupervised deformable image registration with a convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised multi modal image registration via geometry preserving image to image translation")
  Member "quicksilver fast predictive image registration a deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "v net fully convolutional neural networks for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "fast predictive image registration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on biomedical image captioning")

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)