human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "metric learning for image registration"
  Abstract: Image registration is a key technique in medical image analysis to estimate deformations between image pairs. A good deformation model is important for high-quality estimates. However, most existing approaches use ad-hoc deformation models chosen for mathematical convenience rather than to capture observed data variation. Recent deep learning approaches learn deformation models directly from data. However, they provide limited control over the spatial regularity of transformations. Instead of learning the entire registration approach, we learn a spatially-adaptive regularizer within a registration model. This allows controlling the desired level of regularity and preserving structural properties of a registration model. For example, diffeomorphic transformations can be attained. Our approach is a radical departure from existing deep learning approaches to image registration by embedding a deep learning model in an optimization-based registration algorithm to parameterize and data-adapt the registration model itself.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of probabilistic diffeomorphic registration for images and surfaces"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "an unsupervised learning model for deformable medical image registration"
    - "label driven weakly supervised learning for multimodal deformarle image registration"

Connected through hyperedge HE-2 (induced by source paper "region specific diffeomorphic metric mapping"):
  with members:
    - "networks for joint affine and non parametric image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an unsupervised learning model for deformable medical image registration"
    - "non rigid image registration using fully convolutional networks with deep self supervision"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "fast predictive image registration"

Question: Please tell me which class the center node 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)