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 "GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces"):
  Abstract: The similarity join is an important operation for mining high-dimensional feature spaces. Given two data sets, the similarity join computes all tuples ( x, y ) that are within a distance  e .One of the most efficient algorithms for processing similarity-joins is the Multidimensional-Spatial Join (MSJ) by Koudas and Sevcik. In our previous work --- pursued for the two-dimensional case --- we found however that MSJ has several performance shortcomings in terms of CPU and I/O cost as well as memory-requirements. Therefore, MSJ is not generally applicable to high-dimensional data.In this paper, we propose a new algorithm named Generic External Space Sweep (GESS). GESS introduces a modest rate of data replication to reduce the number of expensive distance computations. We present a new cost-model for replication, an I/O model, and an inexpensive method for duplicate removal. The principal component of our algorithm is a highly flexible replication engine.Our analytical model predicts a tremendous reduction of the number of expensive distance computations by several orders of magnitude in comparison to MSJ (factor 10 7 ). In addition, the memory requirements of GESS are shown to be lower by several orders of magnitude. Furthermore, the I/O cost of our algorithm is by factor 2 better (independent from the fact whether replication occurs or not). Our analytical results are confirmed by a large series of simulations and experiments with synthetic and real high-dimensional data sets.

Includes members:
  Member "XXL - A Library Approach to Supporting Efficient Implementations of Advanced Database Queries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Online clustering of parallel data streams")
  Member "Super-EGO: fast multi-dimensional similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Technical Report: KNN Joins Using a Hybrid Approach: Exploiting CPU/GPU Workload Characteristics")

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