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: "using regression techniques to predict large data transfers"
  Abstract: The recent proliferation of Data Grids and the increasingly common practice of using resources as distributed data stores provide a convenient environment for communities of researchers to share, replicate, and manage access to copies of large datasets. This has led to the question of which replica can be accessed most efficiently. In such environments, fetching data from one of the several replica locations requires accurate predictions of end-to-end transfer times. The answer to this question can depend on many factors, including physical characteristics of the resources and the load behavior on the CPUs, networks, and storage devices that are part of the end-to-end data path linking possible sources and sinks. Our approach combines end-to-end application throughput observations with network and disk load variations and captures whole-system performance and variations in load patterns. Our predictions characterize the effect of load variations of several shared devices (network and disk) on file transfer times. We develop a suite of univariate and multivariate predictors that can use multiple data sources to improve the accuracy of the predictions as well as address Data Grid variations (availability of data and sporadic nature of transfers). We ran a large set of data transfer experiments using GridFTP and observed performance predictions within 15% error for our testbed sites, which is quite promising for a pragmatic system.

Connected through hyperedge HE-1 (induced by source paper "predictive performance modeling for distributed batch processing using black box monitoring and machine learning"):
  with members:
    - "job placement advisor based on turnaround predictions for hpc hybrid clouds"
    - "algorithm runtime prediction methods evaluation"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "cloud computing and grid computing 360 degree compared"

Connected through hyperedge HE-2 (induced by source paper "a taxonomy of performance prediction systems in the parallel and distributed computing grids"):
  with members:
    - "a taxonomy of data grids for distributed data sharing management and processing"
    - "mpich g2 a grid enabled implementation of the message passing interface"
    - "gridsim a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing"

Connected through hyperedge HE-3 (induced by source paper "a taxonomy of performance prediction systems in the parallel distributed grids"):
  with members:
    - "mpich g2 a grid enabled implementation of the message passing interface"
    - "gridsim a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing"

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)