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: "Unsupervised Class-Specific Deblurring"
  Abstract: In this paper, we present an end-to-end deblurring network designed specifically for a class of data. Unlike the prior supervised deep-learning works that extensively rely on large sets of paired data, which is highly demanding and challenging to obtain, we propose an unsupervised training scheme with unpaired data to achieve the same. Our model consists of a Generative Adversarial Network (GAN) that learns a strong prior on the clean image domain using adversarial loss and maps the blurred image to its clean equivalent. To improve the stability of GAN and to preserve the image correspondence, we introduce an additional CNN module that reblurs the generated GAN output to match with the blurred input. Along with these two modules, we also make use of the blurred image itself to self-guide the network to constrain the solution space of generated clean images. This self-guidance is achieved by imposing a scale-space gradient error with an additional gradient module. We train our model on different classes and observe that adding the reblur and gradient modules helps in better convergence. Extensive experiments demonstrate that our method performs favorably against the state-of-the-art supervised methods on both synthetic and real-world images even in the absence of any supervision.

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"
    - "Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network"

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