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: "characterizing and subsetting big data workloads"
  Abstract: © 2014 IEEE. Big data benchmark suites must include a diversity of data and workloads to be useful in fairly evaluating big data systems and architectures. However, using truly comprehensive benchmarks poses great challenges for the architecture community. First, we need to thoroughly understand the behaviors of a variety of workloads. Second, our usual simulation-based research methods become prohibitively expensive for big data. As big data is an emerging field, more and more software stacks are being proposed to facilitate the development of big data applications, which aggravates these challenges. In this paper, we first use Principle Component Analysis (PCA) to identify the most important characteristics from 45 metrics to characterize big data workloads from BigDataBench, a comprehensive big data benchmark suite. Second, we apply a clustering technique to the principle components obtained from the PCA to investigate the similarity among big data workloads, and we verify the importance of including different software stacks for big data benchmarking. Third, we select seven representative big data workloads by removing redundant ones and release the BigDataBench simulation version, which is publicly available from http://prof.ict.ac.cn/BigDataBench/ simulatorversion/.

Connected through hyperedge HE-1 (induced by source paper "data motif based proxy benchmarks for big data and ai workloads"):
  with members:
    - "bigdatabench a scalable and unified big data and ai benchmark suite"
    - "data motifs a lens towards fully understanding big data and ai workloads"
    - "fathom reference workloads for modern deep learning methods"
    - "rethinking the inception architecture for computer vision"
    - "the gap benchmark suite"
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-2 (induced by source paper "data motifs a lens towards fully understanding big data and ai workloads"):
  with members:
    - "data motif based proxy benchmarks for big data and ai workloads"
    - "in datacenter performance analysis of a tensor processing unit"
    - "tensorflow a system for large scale machine learning"
    - "very deep convolutional networks for large scale image recognition"
    - "bigdatabench a big data benchmark suite from internet services"
    - "hitting the memory wall implications of the obvious"

Connected through hyperedge HE-3 (induced by source paper "a dwarf based scalable big data benchmarking methodology"):
  with members:
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-4 (induced by source paper "architectural impact on performance of in memory data analytics apache spark case study"):
  with members:
    - "mllib machine learning in apache spark"
    - "performance characterization of in memory data analytics on a modern cloud server"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "hpat high performance analytics with scripting ease of use"):
  with members:
    - "performance characterization of in memory data analytics on a modern cloud server"

Connected through hyperedge HE-6 (induced by source paper "understanding big data analytic workloads on modern processors"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "bigop generating comprehensive big data workloads as a benchmarking framework"
    - "bigdatabench a big data benchmark suite from web search engines"
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-7 (induced by source paper "how data volume affects spark based data analytics on a scale up server"):
  with members:
    - "performance characterization of in memory data analytics on a modern cloud server"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "benchmarking big data systems state of the art and future directions"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "bigdatabench a big data benchmark suite from web search engines"
    - "characterizing data analysis workloads in data centers"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

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)