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: "Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network"
  Abstract: In this paper, we consider numerous low-level vision problems (e.g., edge-preserving filtering and denoising) as recursive image filtering via a hybrid neural network. The network contains several spatially variant recurrent neural networks (RNN) as equivalents of a group of distinct recursive filters for each pixel, and a deep convolutional neural network (CNN) that learns the weights of RNNs. The deep CNN can learn regulations of recurrent propagation for various tasks and effectively guides recurrent propagation over an entire image. The proposed model does not need a large number of convolutional channels nor big kernels to learn features for low-level vision filters. It is significantly smaller and faster in comparison with a deep CNN based image filter. Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm.

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"
    - "Deep Convolutional Neural Network for Image Deconvolution"
    - "Unsupervised Class-Specific Deblurring"

Connected through hyperedge HE-2 (induced by source paper "Deep bilateral learning for real-time image enhancement"):
  with members:
    - "Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter"

Connected through hyperedge HE-3 (induced by source paper "Deep Convolutional Neural Network for Image Deconvolution"):
  with members:
    - "Image Denoising and Inpainting with Deep Neural Networks"
    - "Learning Converged Propagations with Deep Prior Ensemble for 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