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: "Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter"
  Abstract: Deep learning has recently gained high interest in ophthalmology, due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for "active acquisition" embedded deep learning, leading to as high quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning-based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog and cloud layers, the former being performed at a device-level. Improved edge layer performance via "active acquisition" serves as an automatic data curation operator translating to better quality data in electronic health records (EHRs), as well as on the cloud layer, for improved deep learning-based clinical data mining.

Connected through hyperedge HE-1 (induced by source paper "Deep bilateral learning for real-time image enhancement"):
  with members:
    - "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