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 "neurreg neural registration and its application to image segmentation"):
  Abstract: Registration is a fundamental task in medical image analysis which can be applied to several tasks including image segmentation, intra-operative tracking, multi-modal image alignment, and motion analysis. Popular registration tools such as ANTs and NiftyReg optimize an objective function for each pair of images from scratch which is time-consuming for large images with complicated deformation. Facilitated by the rapid progress of deep learning, learning-based approaches such as VoxelMorph have been emerging for image registration. These approaches can achieve competitive performance in a fraction of a second on advanced GPUs. In this work, we construct a neural registration framework, called NeurReg, with a hybrid loss of displacement fields and data similarity, which substantially improves the current state-of-the-art of registrations. Within the framework, we simulate various transformations by a registration simulator which generates fixed image and displacement field ground truth for training. Furthermore, we design three segmentation frameworks based on the proposed registration framework: 1) atlas-based segmentation, 2) joint learning of both segmentation and registration tasks, and 3) multi-task learning with atlas-based segmentation as an intermediate feature. Extensive experimental results validate the effectiveness of the proposed NeurReg framework based on various metrics: the endpoint error (EPE) of the predicted displacement field, mean square error (MSE), normalized local cross-correlation (NLCC), mutual information (MI), Dice coefficient, uncertainty estimation, and the interpretability of the segmentation. The proposed NeurReg improves registration accuracy with fast inference speed, which can greatly accelerate related medical image analysis tasks.

Includes members:
  Member "learning a probabilistic model for diffeomorphic registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation")
  Member "a large annotated medical image dataset for the development and evaluation of segmentation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the future of digital health with federated learning")
  Member "neural multi scale self supervised registration for echocardiogram dense tracking":
    also connected through other hyperedges:
      (none)
  Member "voxelmorph a learning framework for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography")
  Member "unsupervised learning for fast probabilistic diffeomorphic registration":
    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 "airnet self supervised affine registration for 3d medical images using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "networks for joint affine and non parametric image registration")
  Member "an unsupervised learning model for deformable medical image registration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")

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)