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 the value and limits of multi level energy consumption static analysis for deeply embedded single and multi threaded programs"
  Abstract: There is growing interest in lowering the energy consumption of computation. Energy transparency is a concept that makes a program's energy consumption visible from software to hardware through the different system layers. Such transparency can enable energy optimizations at each layer and between layers, and help both programmers and operating systems make energy aware decisions. The common methodology of extracting the energy consumption of a program is through direct measurement of the target hardware. This usually involves specialized equipment and knowledge most programmers do not have. In this paper, we examine how existing methods for static resource analysis and energy modeling can be utilized to perform Energy Consumption Static Analysis (ECSA) for deeply embedded programs. To investigate this, we have developed ECSA techniques that work at the instruction set level and at a higher level, the LLVM IR, through a novel mapping technique. We apply our ECSA to a comprehensive set of mainly industrial benchmarks, including single-threaded and also multi-threaded embedded programs from two commonly used concurrency patterns, task farms and pipelines. We compare our ECSA results to hardware measurements and predictions obtained based on simulation traces. We discuss a number of application scenarios for which ECSA results can provide energy transparency and conclude with a set of new research questions for future work.

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"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "a high level model of embedded flash energy consumption"
    - "static analysis of energy consumption for llvm ir programs"

Connected through hyperedge HE-2 (induced by source paper "entra whole systems energy transparency"):
  with members:
    - "data dependent energy modeling for worst case energy consumption analysis"
    - "inferring energy bounds statically by evolutionary analysis of basic blocks"
    - "probabilistic resource analysis by program transformation"
    - "on the infeasibility of analysing worst case dynamic energy"
    - "fine grained energy modeling for the source code of a mobile application"
    - "probabilistic output analysis by program manipulation"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "modeling and visualizing networked multi core embedded software energy consumption"

Connected through hyperedge HE-3 (induced by source paper "on the infeasibility of analysing worst case dynamic energy"):
  with members:
    - "data dependent energy modelling a worst case perspective"
    - "static analysis of energy consumption for llvm ir programs"

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.PL(Programming Languages)