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 "equi depth histogram construction for big data with quality guarantees"):
  Abstract: The amount of data generated and stored in cloud systems has been increasing exponentially. The examples of data include user generated data, machine generated data as well as data crawled from the Internet. There have been several frameworks with proven efficiency to store and process the petabyte scale data such as Apache Hadoop, HDFS and several NoSQL frameworks. These systems have been widely used in industry and thus are subject to several research. The proposed data processing techniques should be compatible with the above frameworks in order to be practical. One of the key data operations is deriving equi-depth histograms as they are crucial in understanding the statistical properties of the underlying data with many applications including query optimization. In this paper, we focus on approximate equi-depth histogram construction for big data and propose a novel merge based histogram construction method with a histogram processing framework which constructs an equi-depth histogram for a given time interval. The proposed method constructs approximate equi-depth histograms by merging exact equi-depth histograms of partitioned data by guaranteeing a maximum error bound on the number of items in a bucket (bucket size) as well as any range on the histogram.

Includes members:
  Member "only aggressive elephants are fast elephants":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rita an index tuning advisor for replicated databases")
  Member "automatic optimization for mapreduce programs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "streaming space complexity of nearly all functions of one variable on frequency vectors")
  Member "building wavelet histograms on large data in mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quicksel quick selectivity learning with mixture models")

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)