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 implications from benchmarking three big data systems"):
  Abstract: Along with today's data explosion and application diversification, a variety of hardware platforms for data centers are emerging and are attracting interests from both industry and academia. The existing hardware platforms represent a wide range of implementation approaches, and different hardware have different strengths. In this paper, we conduct comprehensive evaluations on three representative data center systems based on BigDataBench, which is a benchmark suite for benchmarking and ranking systems running big data applications. Then we explore the relative performance of the three implementation approaches with different big data applications, and provide strong guidance for the data center system construction. Through our experiments, we has inferred that a data center system based on specific hardware has different performance in the context of different applications and data volumes. When we construct a system, we can take into account not only the performance or energy consumption of the pure hardwares, but also the application-level characteristics. Data scale, application type and complexity should be considered comprehensively when researchers or architects plan to choose fundamental components for their data center system.

Includes members:
  Member "the implications of diverse applications and scalable data sets in benchmarking big data systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bdgs a scalable big data generator suite in big data benchmarking")
  Member "bigdatabench a big data benchmark suite from web search engines":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improving spark application throughput via memory aware task co location a mixture of experts approach")
  Member "characterizing data analysis workloads in data centers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "aibench an industry standard internet service ai benchmark suite")

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.DC(Distributed, Parallel, and Cluster Computing)