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 "Vehicle Localization Using In-Vehicle Pitch Data and Dynamical Models"):
  Abstract: This paper describes a dynamical model-based method for the localization of road vehicles using terrain data from the vehicle's onboard sensors. Road data are encoded using linear dynamical models and then, during travel, the location is identified through continuous comparison of a bank of linear models. The approach presented has several advantages over previous methods described in the literature. First, it creates computationally efficient linear model map representations of the road data. Second, the use of linear models eliminates the need for metrics during the localization process. Third, the localization algorithm is a computationally efficient approach that can have a bounded localization distance in the absence of noise, given certain uniqueness assumptions on the data. Fourth, encoding road data using linear models has the potential to compress the data, while retaining the sensory information. Finally, performing only linear operations on observed noisy data simplifies the creation of noise mitigation algorithms.

Includes members:
  Member "Mobile robot localization by tracking geometric beacons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Robot Localization Using Omnidirectional Color Images")
  Member "IMU-Based Virtual Road Profile Sensor for Vehicle Localization":
    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: Engineering