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: "airlab autograd image registration laboratory"
  Abstract: Medical image registration is an active research topic and forms a basis for many medical image analysis tasks. Although image registration is a rather general concept specialized methods are usually required to target a specific registration problem. The development and implementation of such methods has been tough so far as the gradient of the objective has to be computed. Also, its evaluation has to be performed preferably on a GPU for larger images and for more complex transformation models and regularization terms. This hinders researchers from rapid prototyping and poses hurdles to reproduce research results. There is a clear need for an environment which hides this complexity to put the modeling and the experimental exploration of registration methods into the foreground. With the "Autograd Image Registration Laboratory" (AirLab), we introduce an open laboratory for image registration tasks, where the analytic gradients of the objective function are computed automatically and the device where the computations are performed, on a CPU or a GPU, is transparent. It is meant as a laboratory for researchers and developers enabling them to rapidly try out new ideas for registering images and to reproduce registration results which have already been published. AirLab is implemented in Python using PyTorch as tensor and optimization library and SimpleITK for basic image IO. Therefore, it profits from recent advances made by the machine learning community concerning optimization and deep neural network models. #R##N#The present draft of this paper roughly outlines AirLab with first code snippets and performance analyses. A more exhaustive introduction will follow as a final version soon.

Connected through hyperedge HE-1 (induced by source paper "drmime differentiable mutual information and matrix exponential for multi resolution image registration"):
  with members:
    - "template matching via densities on the roto translation group"
    - "mine mutual information neural estimation"
    - "deep image homography estimation"

Connected through hyperedge HE-2 (induced by source paper "kldivnet an unsupervised neural network for multi modality image registration"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "formal limitations on the measurement of mutual information"
    - "an unsupervised learning model for deformable medical image registration"

Connected through hyperedge HE-3 (induced by source paper "recurrent registration neural networks for deformable image registration"):
  with members:
    - "on the convergence of adam and beyond"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "dilated fcn for multi agent 2d 3d medical image registration"
    - "an artificial agent for robust image registration"
    - "spatial transformer networks"

Connected through hyperedge HE-4 (induced by source paper "image and spatial transformer networks for structure guided image registration"):
  with members:
    - "voxelmorph a learning framework for deformable medical image registration"
    - "a deep learning framework for unsupervised affine and deformable image registration"
    - "weakly supervised convolutional neural networks for multimodal image registration"
    - "birnet brain image registration using dual supervised fully convolutional networks"
    - "an unsupervised learning model for deformable medical image registration"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "spatial transformer networks"

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)