human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Keeping the vehicle on the road: A survey on on-road lane detection systems"):
  Abstract: The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.

Includes members:
  Member "LANA: a lane extraction algorithm that uses frequency domain features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "CLARK, A HETEROGENEOUS SENSOR FUSION METHOD FOR FINDING LANES AND OBSTACLES")
  Member "Connected Car: Technologies, Issues, Future Trends":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Security and privacy for innovative automotive applications: A survey")
  Member "End-To-End Ego Lane Estimation Based on Sequential Transfer Learning for Self-Driving Cars":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Are we ready for autonomous driving? The KITTI vision benchmark suite")
  Member "Towards Efficient Feedback Control in Streaming Computer Vision Pipelines":
    also connected through other hyperedges:
      (none)

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science