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: "some observations on optimal frequency selection in dvfs based energy consumption minimization"
  Abstract: In recent years, the issue of energy consumption in parallel and distributed computing systems has attracted a great deal of attention. In response to this, many energy-aware scheduling algorithms have been developed primarily using the dynamic voltage-frequency scaling (DVFS) capability which has been incorporated into recent commodity processors. Majority of these algorithms involve two passes: schedule generation and slack reclamation. The former pass involves the redistribution of tasks among DVFS-enabled processors based on a given cost function that includes makespan and energy consumption, while the latter pass is typically achieved by executing individual tasks with slacks at a lower processor frequency. In this paper, a new slack reclamation algorithm is proposed by approaching the energy reduction problem from a different angle. Firstly, the problem of task slack reclamation by using combinations of processors' frequencies is formulated. Secondly, several proofs are provided to show that (1) if the working frequency set of processor is assumed to be continues, the optimal energy will be always achieved by using only one frequency, (2) for real processors with a discrete set of working frequencies, the optimal energy is always achieved by using at most two frequencies, and (3) these two frequencies are adjacent/neighbouring when processor energy consumption is a convex function of frequency. Thirdly, a novel algorithm to find the best combination of frequencies to result the optimal energy is presented. The presented algorithm has been evaluated based on results obtained from experiments with three different sets of task graphs: 3000 randomly generated task graphs, and 600 task graphs for two popular applications (Gauss-Jordan and LU decomposition). The results show the superiority of the proposed algorithm in comparison with other techniques.

Connected through hyperedge HE-1 (induced by source paper "energy efficiency in wireless sensor networks"):
  with members:
    - "network load analysis and provisioning of mapreduce applications"
    - "a faster routing scheme for stationary wireless sensor networks a hybrid approach"
    - "secured greedy perimeter stateless routing for wireless sensor networks"

Connected through hyperedge HE-2 (induced by source paper "parameter sensitivity analysis of the energy frequency convexity rule for nanometer scale application processors"):
  with members:
    - "modeling the temperature bias of power consumption for nanometer scale cpus in application processors"
    - "the energy frequency convexity rule modeling and experimental validation on mobile devices"
    - "beebs open benchmarks for energy measurements on embedded platforms"
    - "exploring performance and power properties of modern multicore chips via simple machine models"

Connected through hyperedge HE-3 (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"
    - "on using pattern matching algorithms in mapreduce applications"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a primarily survey on energy efficiency in cloud and distributed computing systems"):
  with members:
    - "multiple frequency selection in dvfs enabled processors to minimize energy consumption"
    - "a task based sensor centeric model for overall energy consumption"
    - "a study on modeling of dependency between configuration parameters and overall energy consumption in wireless sensor network wsn"

Connected through hyperedge HE-5 (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"
    - "on using pattern matching algorithms in mapreduce applications"

Connected through hyperedge HE-6 (induced by source paper "data gathering scheme for wireless sensornetworks using a single mobile element"):
  with members:
    - "on using pattern matching algorithms in mapreduce applications"
    - "modeling overall energy consumption in wireless sensor networks"

Connected through hyperedge HE-7 (induced by source paper "energy aware scheduling using dynamic voltage frequency scaling"):
  with members:
    - "multiple frequency selection in dvfs enabled processors to minimize energy consumption"

Connected through hyperedge HE-8 (induced by source paper "an introduction on dependency between hardware life time components and dynamic voltage scaling"):
  with members:
    - "multiple frequency selection in dvfs enabled processors to minimize energy consumption"

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)