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 high level model of embedded flash energy consumption"
  Abstract: The alignment of code in the flash memory of deeply embedded SoCs can have a large impact on the total energy consumption of a computation. We investigate the effect of code alignment in six SoCs and find that a large proportion of this energy (up to 15% of total SoC energy consumption) can be saved by changes to the alignment. #R##N#A flexible model is created to predict the read-access energy consumption of flash memory on deeply embedded SoCs, where code is executed in place. This model uses the instruction level memory accesses performed by the processor to calculate the flash energy consumption of a sequence of instructions. We derive the model parameters for five SoCs and validate them. The error is as low as 5%, with a 11% average normalized RMS deviation overall. #R##N#The scope for using this model to optimize code alignment is explored across a range of benchmarks and SoCs. Analysis shows that over 30% of loops can be better aligned. This can significantly reduce energy while increasing code size by less than 4%. We conclude that this effect has potential as an effective optimization, saving significant energy in deeply embedded SoCs.

Connected through hyperedge HE-1 (induced by source paper "on the limitations of analyzing worst case dynamic energy of processing"):
  with members:
    - "energy transparency for deeply embedded programs"
    - "data dependent energy modeling for worst case energy consumption analysis"
    - "data dependent energy modelling a worst case perspective"
    - "on the value and limits of multi level energy consumption static analysis for deeply embedded single and multi threaded programs"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "static analysis of energy consumption for llvm ir programs"

Connected through hyperedge HE-2 (induced by source paper "energy transparency for deeply embedded programs"):
  with members:
    - "entra whole systems energy transparency"
    - "data dependent energy modelling a worst case perspective"
    - "static analysis of energy consumption for llvm ir programs"
    - "optimizing the flash ram energy trade off in deeply embedded systems"
    - "bound analysis of imperative programs with the size change abstraction extended version"

Connected through hyperedge HE-3 (induced by source paper "optimizing the flash ram energy trade off in deeply embedded systems"):
  with members:
    - "beebs open benchmarks for energy measurements on embedded platforms"

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.AR(Hardware Architecture)