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 "mutual information neural estimation in cnn based end to end medical image registration"):
  Abstract: Image registration is one of the most underlined processes in medical image analysis. Recently, convolutional neural networks (CNNs) have shown significant potential in both affine and deformable registration. However, the lack of voxel-wise ground truth challenges the training of an accurate CNN-based registration. In this work, we implement a CNN-based mutual information neural estimator for image registration that evaluates the registration outputs in an unsupervised manner. Based on the estimator, we propose an end-to-end registration framework, denoted as MIRegNet, to realize one-shot affine and deformable registration. Furthermore, we propose a weakly supervised network combining mutual information with the Dice similarity coefficients (DSC) loss. We employed a dataset consisting of 190 pairs of 3D pulmonary CT images for validation. Results showed that the MIRegNet obtained an average Dice score of 0.960 for registering the pulmonary images, and the Dice score was further improved to 0.963 when the DSC was included for a weakly supervised learning of image registration.

Includes members:
  Member "a deep learning framework for unsupervised affine and deformable image registration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "fast predictive image registration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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)