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: "Approximate counts and quantiles over sliding windows"
  Abstract: We consider the problem of maintaining e-approximate counts and quantiles over a stream  sliding window  using limited space. We consider two types of sliding windows depending on whether the number of elements  N  in the window is fixed ( fixed-size  sliding window) or variable ( variable-size  sliding window). In a fixed-size sliding window, both the ends of the window slide synchronously over the stream. In a variable-size sliding window, an adversary slides the window ends independently, and therefore has the ability to vary the number of elements  N  in the window.We present various deterministic and randomized algorithms for approximate counts and quantiles. All of our algorithms require  O (1/e polylog(1/e,  N )) space. For quantiles, this space requirement is an improvement over the previous best bound of  O (1/e 2  polylog(1/e,  N )). We believe that no previous work on space-efficient approximate counts over sliding windows exists.

Connected through hyperedge HE-1 (induced by source paper "New sampling-based summary statistics for improving approximate query answers"):
  with members:
    - "Stream sampling for variance-optimal estimation of subset sums"
    - "Dynamic generation of discrete random variates"
    - "A geometric approach to monitoring threshold functions over distributed data streams"
    - "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-2 (induced by source paper "Random sampling techniques for space efficient online computation of order statistics of large datasets"):
  with members:
    - "New sampling-based summary statistics for improving approximate query answers"
    - "PLANET: massively parallel learning of tree ensembles with MapReduce"

Connected through hyperedge HE-3 (induced by source paper "A geometric approach to monitoring threshold functions over distributed data streams"):
  with members:
    - "New sampling-based summary statistics for improving approximate query answers"
    - "Probabilistic Threshold Based Monitoring Using Sensor Networks"

Connected through hyperedge HE-4 (induced by source paper "Resource sharing in continuous sliding-window aggregates"):
  with members:
    - "A unified framework for monitoring data streams in real time"

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: Mathematics