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: "accelerating large scale data exploration through data diffusion"
  Abstract: Data-intensive applications often require exploratory analysis of large datasets. If analysis is performed on distributed resources, data locality can be crucial to high throughput and performance. We propose a "data diffusion" approach that acquires compute and storage resources dynamically, replicates data in response to demand, and schedules computations close to data. As demand increases, more resources are acquired, thus allowing faster response to subsequent requests that refer to the same data; when demand drops, resources are released. This approach can provide the benefits of dedicated hardware without the associated high costs, depending on workload and resource characteristics. The approach is reminiscent of cooperative caching, web-caching, and peer-to-peer storage systems, but addresses different application demands. Other data-aware scheduling approaches assume dedicated resources, which can be expensive and/or inefficient if load varies significantly. To explore the feasibility of the data diffusion approach, we have extended the Falkon resource provisioning and task scheduling system to support data caching and data-aware scheduling. Performance results from both micro-benchmarks and a large scale astronomy application demonstrate that our approach improves performance relative to alternative approaches, as well as provides improved scalability as aggregated I/O bandwidth scales linearly with the number of data cache nodes.

Connected through hyperedge HE-1 (induced by source paper "pilot abstraction a valid abstraction for data intensive applications on hpc hadoop and cloud infrastructures"):
  with members:
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "api design for machine learning software experiences from the scikit learn project"
    - "mli an api for distributed machine learning"
    - "p a model of pilot abstractions"

Connected through hyperedge HE-2 (induced by source paper "cloud computing and grid computing 360 degree compared"):
  with members:
    - "the community authorization service status and future"
    - "a community authorization service for group collaboration"
    - "mpich g2 a grid enabled implementation of the message passing interface"
    - "the anatomy of the grid enabling scalable virtual organizations"
    - "nimrod g an architecture for a resource management and scheduling system in a global computational grid"

Connected through hyperedge HE-3 (induced by source paper "data diffusion dynamic resource provision and data aware scheduling for data intensive applications"):
  with members:
    - "realizing fast scalable and reliable scientific computations in grid environments"
    - "distributed computing economics"

Connected through hyperedge HE-4 (induced by source paper "scientific workflow systems for 21st century e science new bottle or new wine"):
  with members:
    - "distributed computing economics"

Connected through hyperedge HE-5 (induced by source paper "enabling loosely coupled serial job execution on the ibm bluegene p supercomputer and the sicortex sc5832"):
  with members:
    - "realizing fast scalable and reliable scientific computations in grid environments"

Connected through hyperedge HE-6 (induced by source paper "realizing fast scalable and reliable scientific computations in grid environments"):
  with members:
    - "turning cluster management into data management a system overview"

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)