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 geometric approach to monitoring threshold functions over distributed data streams"
  Abstract: Monitoring data streams in a distributed system is the focus of much research in recent years. Most of the proposed schemes, however, deal with monitoring simple aggregated values, such as the frequency of appearance of items in the streams. More involved challenges, such as the important task of feature selection (e.g., by monitoring the information gain of various features), still require very high communication overhead using naive, centralized algorithms.   We present a novel geometric approach which reduces monitoring the value of a function (vis-a-vis a threshold) to a set of constraints applied locally on each of the streams. The constraints are used to locally filter out data increments that do not affect the monitoring outcome, thus avoiding unnecessary communication. As a result, our approach enables monitoring of arbitrary threshold functions over distributed data streams in an efficient manner.   We present experimental results on real-world data which demonstrate that our algorithms are highly scalable, and considerably reduce communication load in comparison to centralized algorithms.

Connected through hyperedge HE-1 (induced by source paper "Approximate counts and quantiles over sliding windows"):
  with members:
    - "Resource sharing in continuous sliding-window aggregates"
    - "New sampling-based summary statistics for improving approximate query answers"
    - "Random sampling techniques for space efficient online computation of order statistics of large datasets"

Connected through hyperedge HE-2 (induced by source paper "New sampling-based summary statistics for improving approximate query answers"):
  with members:
    - "Approximate counts and quantiles over sliding windows"
    - "Stream sampling for variance-optimal estimation of subset sums"
    - "Dynamic generation of discrete random variates"
    - "Random sampling techniques for space efficient online computation of order statistics of large datasets"
    - "Efficient Compression and Storage of XML OLAP Cubes"
    - "Gossip-based distribution estimation in peer-to-peer networks"

Connected through hyperedge HE-3 (induced by source paper "Probabilistic Threshold Based Monitoring Using Sensor Networks"):
  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