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: "on using pattern matching algorithms in mapreduce applications"
  Abstract: In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW)is utilized for such comparison, a correlation analysis is then applied to DTWs' outcomes to produce feasible similarity patterns. Three real applications (Word Count, Exim Mainlogparsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms

Connected through hyperedge HE-1 (induced by source paper "on modeling dependency between mapreduce configuration parameters and total execution time"):
  with members:
    - "a task based sensor centeric model for overall energy consumption"
    - "modeling overall energy consumption in wireless sensor networks"
    - "a study on modeling of dependency between configuration parameters and overall energy consumption in wireless sensor network wsn"
    - "some observations on optimal frequency selection in dvfs based energy consumption minimization"

Connected through hyperedge HE-2 (induced by source paper "statistical analysis to extract effective parameters on overall energy consumption of wireless sensor network wsn"):
  with members:
    - "on modelling and prediction of total cpu usage for applications in mapreduce environments"
    - "a task based sensor centeric model for overall energy consumption"
    - "some observations on optimal frequency selection in dvfs based energy consumption minimization"

Connected through hyperedge HE-3 (induced by source paper "data gathering scheme for wireless sensornetworks using a single mobile element"):
  with members:
    - "modeling overall energy consumption in wireless sensor networks"
    - "some observations on optimal frequency selection in dvfs based energy consumption minimization"

Connected through hyperedge HE-4 (induced by source paper "network load analysis and provisioning of mapreduce applications"):
  with members:
    - "on modeling dependency between mapreduce configuration parameters and total execution time"
    - "a study on using uncertain time series matching algorithms in mapreduce applications"

Connected through hyperedge HE-5 (induced by source paper "on modelling and prediction of total cpu usage for applications in mapreduce environments"):
  with members:
    - "a study on using uncertain time series matching algorithms in mapreduce applications"

Connected through hyperedge HE-6 (induced by source paper "thesis report resource utilization provisioning in mapreduce"):
  with members:
    - "on modeling dependency between mapreduce configuration parameters and total execution time"

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)