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 "benchmarking big data systems state of the art and future directions"):
  Abstract: The great prosperity of big data systems such as Hadoop in recent years makes the benchmarking of these systems become crucial for both research and industry communities. The complexity, diversity, and rapid evolution of big data systems gives rise to various new challenges about how we design generators to produce data with the 4V properties (i.e. volume, velocity, variety and veracity), as well as implement application-specific but still comprehensive workloads. However, most of the existing big data benchmarks can be described as attempts to solve specific problems in benchmarking systems. This article investigates the state-of-the-art in benchmarking big data systems along with the future challenges to be addressed to realize a successful and efficient benchmark.

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 "characterizing and subsetting big data workloads":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data motif based proxy benchmarks for big data and ai workloads")
  Member "bigdatabench a big data benchmark suite from web search engines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "aibench an industry standard internet service ai benchmark suite")
  Member "interactive analytical processing in big data systems a cross industry study of mapreduce workloads":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vat asymptotic cost analysis for multi level key value stores")

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.PF(Performance)