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 "improving distributed similarity join in metric space with error bounded sampling"):
  Abstract: Given two sets of objects, metric similarity join finds all similar pairs of objects according to a particular distance function in metric space. There is an increasing demand to provide a scalable similarity join framework which can support efficient query and analytical services in the era of Big Data. The existing distributed metric similarity join algorithms adopt random sampling techniques to produce pivots and utilize holistic partitioning methods based on the generated pivots to partition data, which results in data skew problem since both the generated pivots and the partition strategies have no quality guarantees. #R##N#To address the limitation, we propose SP-Join, an end-to-end framework to support distributed similarity join in metric space based on the MapReduce paradigm, which (i) employs an estimation-based stratified sampling method to produce pivots with quality guarantees for any sample size, and (ii) devises an effective cost model as the guideline to split the whole datasets into partition in map and reduce phases according to the sampled pivots. Although obtaining an optimal set of partitions is proven to be NP-Hard, SP-Join adopts efficient partitioning strategies based on such a cost model to achieve an even partition with explainable quality. We implement our framework upon Apache Spark platform and conduct extensive experiments on four real-world datasets. The results show that our method significantly outperforms state-of-the-art methods.

Includes members:
  Member "verdictdb universalizing approximate query processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video monitoring queries")
  Member "adaptive sampling for rapidly matching histograms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards scalable dataframe systems")
  Member "mapreduce and streaming algorithms for diversity maximization in metric spaces of bounded doubling dimension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "coreset based strategies for robust center type problems")
  Member "efficient processing of k nearest neighbor joins using mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on geographically distributed big data processing using mapreduce")
  Member "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable similarity joins of tokenized strings")
  Member "load balancing for mapreduce based entity resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blocking and filtering techniques for entity resolution a survey")

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)