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: "a simulator for data intensive job scheduling"
  Abstract: Despite the fact that size-based schedulers can give excellent results in terms of both average response times and fairness, data-intensive computing execution engines generally do not employ size-based schedulers, mainly because of the fact that job size is not known a priori. #R##N#In this work, we perform a simulation-based analysis of the performance of size-based schedulers when they are employed with the workload of typical data-intensive schedules and with approximated size estimations. We show results that are very promising: even when size estimation is very imprecise, response times of size-based schedulers can be definitely smaller than those of simple scheduling techniques such as processor sharing or FIFO.

Connected through hyperedge HE-1 (induced by source paper "scheduling with inexact job sizes the merits of shortest processing time first"):
  with members:
    - "revisiting size based scheduling with estimated job sizes"
    - "psbs practical size based scheduling"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-2 (induced by source paper "psbs practical size based scheduling"):
  with members:
    - "revisiting size based scheduling with estimated job sizes"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-3 (induced by source paper "revisiting size based scheduling with estimated job sizes"):
  with members:
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

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)