human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the zones algorithm for finding points near a point or cross matching spatial datasets"):
  Abstract: Abstract: Zones index an N-dimensional Euclidian or metric space to efficiently support points-near-a-point queries either within a dataset or between two datasets. The approach uses relational algebra and the B-Tree mechanism found in almost all relational database systems. Hence, the Zones Algorithm gives a portable-relational implementation of points-near-point, spatial cross-match, and self-match queries. This article corrects some mistakes in an earlier article we wrote on the Zones Algorithm and describes some algorithmic improvements. The Appendix includes an implementation of point-near-point , self-match , and cross-match using the USGS city and stream gauge database. 1. Introduction The article “There Goes the Neighborhood: Relational Algebra for Spatial Data Search” [1] introduced three spatial indexing methods for points and regions on a sphere: (1) Hierarchical Triangular Mesh (HTM) that is good for point-near-point and point-in-region queries with high dynamic range in region sizes, (2)

Includes members:
  Member "indexing the sphere with the hierarchical triangular mesh":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient classification of billions of points into complex geographic regions using hierarchical triangular mesh")
  Member "when database systems meet the grid":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "where the rubber meets the sky bridging the gap between databases and science")
  Member "there goes the neighborhood relational algebra for spatial data search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "supporting finite element analysis with a relational database backend part i there is life beyond files")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.DB(Databases)