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 task based sensor centeric model for overall energy consumption"
  Abstract: Sensors have limited resources so it is important to manage the resources efficiently to maximize their use. A sensor's battery is a crucial resource as it singly determines the lifetime of sensor network applications. Since these devices are useful only when they are able to communicate with the world, radio transceiver of a sensor as an I/O and a costly unit plays a key role in its lifetime. This resource often consumes a big portion of the sensor's energy as it must be active most of the time to announce the existence of the sensor in the network. As such the radio component has to deal with its embedded sensor network whose parameters and operations have significant effects on the sensor's lifetime. In existing energy models, hardware is considered, but the environment and the network's parameters did not receive adequate attention. Energy consumption components of traditional network architecture are often considered individually and separately, and their influences on each other have not been considered in these approaches. In this paper we consider all possible tasks of a sensor in its embedded network and propose an energy management model. We categorize these tasks in five energy consuming constituents. The sensor's Energy Consumption (EC) is modeled on its energy consuming constituents and their input parameters and tasks. The sensor's EC can thus be reduced by managing and executing efficiently the tasks of its constituents. The proposed approach can be effective for power management, and it also can be used to guide the design of energy efficient wireless sensor networks through network parameterization and optimization.

Connected through hyperedge HE-1 (induced by source paper "on modeling dependency between mapreduce configuration parameters and total execution time"):
  with members:
    - "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"
    - "some observations on optimal frequency selection in dvfs based energy consumption minimization"

Connected through hyperedge HE-2 (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 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-3 (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"
    - "on using pattern matching algorithms in mapreduce applications"
    - "some observations on optimal frequency selection in dvfs based energy consumption minimization"

Connected through hyperedge HE-4 (induced by source paper "multiple frequency selection in dvfs enabled processors to minimize energy consumption"):
  with members:
    - "modeling overall energy consumption in wireless sensor networks"

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.NI(Networking and Internet Architecture)