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 "bdgs a scalable big data generator suite in big data benchmarking"):
  Abstract: Data generation is a key issue in big data benchmarking that aims to generate application-specific data sets to meet the 4V requirements of big data. Specifically, big data generators need to generate scalable data (Volume) of different types (Variety) under controllable generation rates (Velocity) while keeping the important characteristics of raw data (Veracity). This gives rise to various new challenges about how we design generators efficiently and successfully. To date, most existing techniques can only generate limited types of data and support specific big data systems such as Hadoop. Hence we develop a tool, called Big Data Generator Suite (BDGS), to efficiently generate scalable big data while employing data models derived from real data to preserve data veracity. The effectiveness of BDGS is demonstrated by developing six data generators covering three representative data types (structured, semi-structured and unstructured) and three data sources (text, graph, and table data).

Includes members:
  Member "bigdatabench a big data benchmark suite from internet services":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tlb and pagewalk performance in multicore architectures with large die stacked dram cache")
  Member "bigop generating comprehensive big data workloads as a benchmarking framework":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bestconfig tapping the performance potential of systems via automatic configuration tuning")
  Member "the implications of diverse applications and scalable data sets in benchmarking big data systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "characterizing and subsetting big data workloads")
  Member "characterizing data analysis workloads in data centers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "aibench an industry standard internet service ai benchmark suite")
  Member "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding big data analytic workloads on modern processors")

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)