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: "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.

Connected through hyperedge HE-1 (induced by source paper "Mobile robot localization by tracking geometric beacons"):
  with members:
    - "Sensor models and multisensor integration"
    - "Predictive autonomous robot navigation"
    - "Global A-Optimal Robot Exploration in SLAM"
    - "Robot Localization Using Omnidirectional Color Images"

Connected through hyperedge HE-2 (induced by source paper "IMU-Based Virtual Road Profile Sensor for Vehicle Localization"):
  with members:
    (none)

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: Engineering