human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Approximate counts and quantiles over sliding windows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "New sampling-based summary statistics for improving approximate query answers")
  Member "New sampling-based summary statistics for improving approximate query answers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Dynamic generation of discrete random variates")
  Member "PLANET: massively parallel learning of tree ensembles with MapReduce":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Acceleration of Data Streaming Classification using Reconfigurable Technology")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science