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: "Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes"
  Abstract: A fundamental issue in active learning of Gaussian processes is that of the exploration-exploitation trade-off. This paper presents a novel nonmyopic e-Bayes-optimal active learning (e-BAL) approach that jointly and naturally optimizes the trade-off. In contrast, existing works have primarily developed myopic/greedy algorithms or performed exploration and exploitation separately. To perform active learning in real time, we then propose an anytime algorithm based on e-BAL with performance guarantee and empirically demonstrate using synthetic and real-world datasets that, with limited budget, it outperforms the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "Parallel gaussian process regression for big data: low-rank representation meets markov approximation"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Efficient informative sensing using multiple robots"):
  with members:
    - "Global A-Optimal Robot Exploration in SLAM"

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