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 clustering of parallel data streams"
  Abstract: In recent years, the management and processing of so-called data streams has become a topic of active research in several fields of computer science such as, e.g., distributed systems, database systems, and data mining. A data stream can roughly be thought of as a transient, continuously increasing sequence of time-stamped data. In this paper, we consider the problem of clustering parallel streams of real-valued data, that is to say, continuously evolving time series. In other words, we are interested in grouping data streams the evolution over time of which is similar in a specific sense. In order to maintain an up-to-date clustering structure, it is necessary to analyze the incoming data in an online manner, tolerating not more than a constant time delay. For this purpose, we develop an efficient online version of the classical K-means clustering algorithm. Our method's efficiency is mainly due to a scalable online transformation of the original data which allows for a fast computation of approximate distances between streams.

Connected through hyperedge HE-1 (induced by source paper "XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries"):
  with members:
    - "GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces"

Connected through hyperedge HE-2 (induced by source paper "Towards combinatorial clustering: preliminary research survey."):
  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