human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Deep Convolutional Neural Network for Image Deconvolution"
  Abstract: Many fundamental image-related problems involve deconvolution operators. Real blur degradation seldom complies with an ideal linear convolution model due to camera noise, saturation, image compression, to name a few. Instead of perfectly modeling outliers, which is rather challenging from a generative model perspective, we develop a deep convolutional neural network to capture the characteristics of degradation. We note directly applying existing deep neural networks does not produce reasonable results. Our solution is to establish the connection between traditional optimization-based schemes and a neural network architecture where a novel, separable structure is introduced as a reliable support for robust deconvolution against artifacts. Our network contains two submodules, both trained in a supervised manner with proper initialization. They yield decent performance on non-blind image deconvolution compared to previous generative-model based methods.

Connected through hyperedge HE-1 (induced by source paper "Image Denoising and Inpainting with Deep Neural Networks"):
  with members:
    - "Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries"
    - "Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network"
    - "Unsupervised Class-Specific Deblurring"

Connected through hyperedge HE-2 (induced by source paper "Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network"):
  with members:
    - "Image Denoising and Inpainting with Deep Neural Networks"
    - "Deep bilateral learning for real-time image enhancement"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science