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: "in cloud can scientific communities benefit from the economies of scale"
  Abstract: The basic idea behind Cloud computing is that resource providers offer elastic resources to end users. In this paper, we intend to answer one key question to the success of Cloud computing: in Cloud, can small or medium-scale scientific computing communities benefit from the economies of scale? Our research contributions are three-fold: first, we propose an enhanced scientific public cloud model (ESP) that encourages small- or medium-scale organizations to rent elastic resources from a public cloud provider; second, on a basis of the ESP model, we design and implement the DawningCloud system that can consolidate heterogeneous scientific workloads on a Cloud site; third, we propose an innovative emulation methodology and perform a comprehensive evaluation. We found that for two typical workloads: high throughput computing (HTC) and many task computing (MTC), DawningCloud saves the resource consumption maximally by 44.5% (HTC) and 72.6% (MTC) for service providers, and saves the total resource consumption maximally by 47.3% for a resource provider with respect to the previous two public Cloud solutions. To this end, we conclude that for typical workloads: HTC and MTC, DawningCloud can enable scientific communities to benefit from the economies of scale of public Clouds.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-3 (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"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-4 (induced by source paper "characterizing data analysis workloads 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 "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"):
  with members:
    - "precise request tracing and performance debugging for multi tier services of black boxes"

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)