human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "the implications of diverse applications and scalable data sets in benchmarking big data systems"
  Abstract: Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we focus on measuring the performance impacts of diverse applications and scalable volumes of data sets on big data systems. For four typical data analysis applications---an important class of big data applications, we find two major results through experiments: first, the data scale has a significant impact on the performance of big data systems, so we must provide scalable volumes of data sets in big data benchmarks. Second, for the four applications, even all of them use the simple algorithms, the performance trends are different with increasing data scales, and hence we must consider not only variety of data sets but also variety of applications in benchmarking big data systems.

Connected through hyperedge HE-1 (induced by source paper "bdgs a scalable big data generator suite in big data benchmarking"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "bigop generating comprehensive big data workloads as a benchmarking framework"
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-2 (induced by source paper "characterizing and subsetting big data workloads"):
  with members:
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing data analysis workloads in data centers"
    - "in cloud can scientific communities benefit from the economies of scale"

Connected through hyperedge HE-3 (induced by source paper "bigdatabench a big data benchmark suite from web search engines"):
  with members:
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"
    - "in cloud can scientific communities benefit from the economies of scale"

Connected through hyperedge HE-4 (induced by source paper "axpue application level metrics for power usage effectiveness in data centers"):
  with members:
    - "bigdatabench a big data benchmark suite from web search engines"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-5 (induced by source paper "the implications from benchmarking three big data systems"):
  with members:
    - "bigdatabench a big data benchmark suite from web search engines"
    - "characterizing data analysis workloads in data centers"

Question: Please tell me which class the center node 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)