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: "Developing Vision-based and Cooperative Vehicular Embedded Systems for Enhancing Road Monitoring Services"
  Abstract: Abstract   This paper presents a generic processing architecture as well as tools and approaches for supporting road monitoring services. More precisely, a small-scale simulator is presented for experimenting the design and the implementation of vision-based vehicular embedded systems in indoor environments, i.e. a test bench for embedded system laboratories. This simulator employs embedded electronic systems, sensors as well as hybrid communication and computational technologies. In particular, these technologies are exploited in order to produce our cooperative and real-time embedded system for enhancing road monitoring services. This type of simulator can be used for developing a variety of innovative road monitoring services based on the detection of dynamical points of interest. Notably, a system has been implemented in the case of a simulated road service to aid police agency for the recognition of wanted individuals and stolen vehicles. Experimental results show the potential of the simulator for fostering the development of a next generation of vehicular embedded system as well as for the emergence of participatory road services.

Connected through hyperedge HE-1 (induced by source paper "Are we ready for autonomous driving? The KITTI vision benchmark suite"):
  with members:
    - "End-To-End Ego Lane Estimation Based on Sequential Transfer Learning for Self-Driving Cars"
    - "Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation"
    - "SegFlow: Joint Learning for Video Object Segmentation and Optical Flow"
    - "Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames"
    - "WildDash - Creating Hazard-Aware Benchmarks"
    - "Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement"
    - "Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data."
    - "A Flexible High-Resolution Real-Time Low-Power Stereo Vision Engine"

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