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: "Random sampling techniques for space efficient online computation of order statistics of large datasets"
  Abstract: In a recent paper [MRL98], we had described a general framework for single pass approximate quantile finding algorithms. This framework included several known algorithms as special cases. We had identified a new algorithm, within the framework, which had a significantly smaller requirement for main memory than other known algorithms. In this paper, we address two issues left open in our earlier paper.  First, all known and space efficient algorithms for approximate quantile finding require advance knowledge of the length of the input sequence. Many important database applications employing quantiles cannot provide this information. In this paper, we present a novel non-uniform random sampling scheme and an extension of our framework. Together, they form the basis of a new algorithm which computes approximate quantiles without knowing the input sequence length.  Second, if the desired quantile is an extreme value (e.g., within the top 1% of the elements), the space requirements of currently known algorithms are overly pessimistic. We provide a simple algorithm which estimates extreme values using less space than required by the earlier more general technique for computing all quantiles. Our principal observation here is that random sampling is quantifiably better when estimating extreme values than is the case with the median.

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"
    - "A geometric approach to monitoring threshold functions over distributed data streams"

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"
    - "A geometric approach to monitoring threshold functions over distributed data streams"
    - "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 "PLANET: massively parallel learning of tree ensembles with MapReduce"):
  with members:
    - "Acceleration of Data Streaming Classification using Reconfigurable Technology"

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