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: "deep learning for photoacoustic tomography from sparse data"
  Abstract: The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacoustic tomography (PAT). We develop a direct and highly efficient reconstruction algorithm based on deep learning. In our approach image reconstruction is performed with a deep convolutional neural network (CNN), whose weights are adjusted prior to the actual image reconstruction based on a set of training data. The proposed reconstruction approach can be interpreted as a network that uses the PAT filtered backprojection algorithm for the first layer, followed by the U-net architecture for the remaining layers. Actual image reconstruction with deep learning consists in one evaluation of the trained CNN, which does not require time consuming solution of the forward and adjoint problems. At the same time, our numerical results demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative approaches for PAT from sparse data.

Connected through hyperedge HE-1 (induced by source paper "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"):
  with members:
    - "model based learning for accelerated limited view 3d photoacoustic tomography"
    - "deep convolutional neural network for inverse problems in imaging"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "deep residual learning for compressed sensing ct reconstruction via persistent homology analysis"
    - "u net convolutional networks for biomedical image segmentation"
    - "accurate image super resolution using very deep convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "fast enhanced ct metal artifact reduction using data domain deep learning"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "cnn based segmentation of medical imaging data"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "can fully convolutional networks perform well for general image restoration problems"

Connected through hyperedge HE-3 (induced by source paper "deep learning diffuse optical tomography"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "efficient and accurate inversion of multiple scattering with deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "networks for nonlinear diffusion problems in imaging"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "real time cardiovascular mr with spatio temporal artifact suppression using deep learning proof of concept in congenital heart disease"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "image reconstruction by domain transform manifold learning"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-5 (induced by source paper "random mesh projectors for inverse problems"):
  with members:
    - "adversarial regularizers in inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "deep image prior"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep learning guided image reconstruction from incomplete data"
    - "maximal sparsity with deep networks"
    - "identity mappings in deep residual networks"
    - "amortised map inference for image super resolution"

Connected through hyperedge HE-6 (induced by source paper "machine friendly machine learning interpretation of computed tomography without image reconstruction"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "phase recovery and holographic image reconstruction using deep learning in neural networks"):
  with members:
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"
    - "image super resolution using deep convolutional networks"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-8 (induced by source paper "deep learning enhanced mobile phone microscopy"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "deep learning microscopy"
    - "deep convolutional neural network for inverse problems in imaging"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

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)