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: "Online reliability assessment and reliability-aware fusion for Ego-Lane detection using influence diagram and Bayes filter"
  Abstract: Within the context of road estimation, the present paper addresses the problem of the fusion of several sources with different reliabilities. Thereby, reliability represents a higher-level uncertainty. This problem arises in automated driving and ADAS due to changing environmental conditions, e.g., road type or visibility of lane markings. Thus, we present an online sensor reliability assessment and reliability-aware fusion to cope with this challenge. First, we apply a boosting algorithm to select the highly discriminant features among the extracted information. Using them we apply different classifiers to learn the reliabilities, such as Bayesian Network and Random Forest classifiers. To stabilize the estimated reliabilities over time, we deploy approaches such as Dempster-Shafer evidence theory and Influence Diagram combined with a Bayes Filter. Using a big collection of real data recordings, the experimental results support our proposed approach.

Connected through hyperedge HE-1 (induced by source paper "A survey of performance measures to evaluate ego-lane estimation and a novel sensor-independent measure along with its applications"):
  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: Computer science