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: "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"
  Abstract: For the first time, this paper systematically identifies three categories of throughput oriented workloads in data centers: services, data processing applications, and interactive real-time applications, whose targets are to increase the volume of throughput in terms of processed requests or data, or supported maximum number of simultaneous subscribers, respectively, and we coins a new term high volume throughput computing (in short HVC) to describe those workloads and data center systems designed for them. We characterize and compare HVC with other computing paradigms, e.g., high throughput computing, warehouse-scale computing, and cloud computing, in terms of levels, workloads, metrics, coupling degree, data scales, and number of jobs or service instances. We also preliminarily report our ongoing work on the metrics and benchmarks for HVC systems, which is the foundation of designing innovative data center systems for HVC workloads.

Connected through hyperedge HE-1 (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"
    - "characterizing and subsetting big data workloads"
    - "bigdatabench a big data benchmark suite from web search engines"
    - "characterizing data analysis workloads in data centers"

Connected through hyperedge HE-2 (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"
    - "the implications of diverse applications and scalable data sets in benchmarking big data systems"
    - "characterizing data analysis workloads in data centers"

Connected through hyperedge HE-3 (induced by source paper "bigdatabench a big data benchmark suite from internet services"):
  with members:
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "bigdatabench a big data benchmark suite from web search engines"
    - "characterizing data analysis workloads in data centers"

Connected through hyperedge HE-4 (induced by source paper "the implications of diverse applications and scalable data sets in benchmarking big data systems"):
  with members:
    - "bigdatabench a big data benchmark suite from web search engines"
    - "characterizing data analysis workloads in data centers"
    - "in cloud can scientific communities benefit from the economies of scale"

Connected through hyperedge HE-5 (induced by source paper "bigdatabench a big data benchmark suite from web search engines"):
  with members:
    - "the implications of diverse applications and scalable data sets in benchmarking big data systems"
    - "characterizing data analysis workloads in data centers"
    - "in cloud can scientific communities benefit from the economies of scale"

Connected through hyperedge HE-6 (induced by source paper "axpue application level metrics for power usage effectiveness in data centers"):
  with members:
    - "the implications of diverse applications and scalable data sets in benchmarking big data systems"
    - "bigdatabench a big data benchmark suite from web search engines"

Connected through hyperedge HE-7 (induced by source paper "characterizing data analysis workloads in data centers"):
  with members:
    - "bigdatabench a big data benchmark suite from web search engines"
    - "in cloud can scientific communities benefit from the economies of scale"

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)