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: "A review on time series data mining"
  Abstract: Time series is an important class of temporal data objects and it can be easily obtained from scientific and financial applications. A time series is a collection of observations made chronologically. The nature of time series data includes: large in data size, high dimensionality and necessary to update continuously. Moreover time series data, which is characterized by its numerical and continuous nature, is always considered as a whole instead of individual numerical field. The increasing use of time series data has initiated a great deal of research and development attempts in the field of data mining. The abundant research on time series data mining in the last decade could hamper the entry of interested researchers, due to its complexity. In this paper, a comprehensive revision on the existing time series data mining research is given. They are generally categorized into representation and indexing, similarity measure, segmentation, visualization and mining. Moreover state-of-the-art research issues are also highlighted. The primary objective of this paper is to serve as a glossary for interested researchers to have an overall picture on the current time series data mining development and identify their potential research direction to further investigation.

Connected through hyperedge HE-1 (induced by source paper "Efficient Similarity Search over Future Stream Time Series"):
  with members:
    - "A unified framework for monitoring data streams in real time"

Connected through hyperedge HE-2 (induced by source paper "Piecewise statistic approximation based similarity measure for time series"):
  with members:
    - "Time-series clustering based on linear fuzzy information granules"

Connected through hyperedge HE-3 (induced by source paper "A multi-wavelet optimization approach using similarity measures for electrocardiogram signal classification"):
  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