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: "bigop generating comprehensive big data workloads as a benchmarking framework"
  Abstract: Big Data is considered proprietary asset of companies, organizations, and even nations. Turning big data into real treasure requires the support of big data systems. A variety of commercial and open source products have been unleashed for big data storage and processing. While big data users are facing the choice of which system best suits their needs, big data system developers are facing the question of how to evaluate their systems with regard to general big data processing needs. System benchmarking is the classic way of meeting the above demands. However, existent big data benchmarks either fail to represent the variety of big data processing requirements, or target only one specific platform, e.g. Hadoop. #R##N#In this paper, with our industrial partners, we present BigOP, an end-to-end system benchmarking framework, featuring the abstraction of representative Operation sets, workload Patterns, and prescribed tests. BigOP is part of an open-source big data benchmarking project, BigDataBench. BigOP's abstraction model not only guides the development of BigDataBench, but also enables automatic generation of tests with comprehensive workloads. #R##N#We illustrate the feasibility of BigOP by implementing an automatic test generation tool and benchmarking against three widely used big data processing systems, i.e. Hadoop, Spark and MySQL Cluster. Three tests targeting three different application scenarios are prescribed. The tests involve relational data, text data and graph data, as well as all operations and workload patterns. We report results following test specifications.

Connected through hyperedge HE-1 (induced by source paper "bestconfig tapping the performance potential of systems via automatic configuration tuning"):
  with members:
    - "acts in need automatic configuration tuning with scalability guarantees"
    - "aloja ml a framework for automating characterization and knowledge discovery in hadoop deployments"
    - "the asilomar report on database research"

Connected through hyperedge HE-2 (induced by source paper "understanding big data analytic workloads on modern processors"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "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-3 (induced by source paper "identifying dwarfs workloads in big data analytics"):
  with members:
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-4 (induced by source paper "managing analyzing large volumes of dynamic diverse data"):
  with members:
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-5 (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"
    - "the implications of diverse applications and scalable data sets in benchmarking big data systems"
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented 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.DC(Distributed, Parallel, and Cluster Computing)