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: "energy transparency for deeply embedded programs"
  Abstract: Energy transparency is a concept that makes a program’s energy consumption visible, from hardware up to software, through the different system layers. Such transparency can enable energy optimizations at each layer and between layers, as well as help both programmers and operating systems make energy-aware decisions. In this article, we focus on deeply embedded devices, typically used for Internet of Things (IoT) applications, and demonstrate how to enable energy transparency through existing static resource analysis (SRA) techniques and a new target-agnostic profiling technique, without hardware energy measurements. Our novel mapping technique enables software energy consumption estimations at a higher level than the Instruction Set Architecture (ISA), namely the LLVM intermediate representation (IR) level, and therefore introduces energy transparency directly to the LLVM optimizer. We apply our energy estimation techniques to a comprehensive set of benchmarks, including single- and multithreaded embedded programs from two commonly used concurrency patterns: task farms and pipelines. Using SRA, our LLVM IR results demonstrate a high accuracy with a deviation in the range of 1% from the ISA SRA. Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of 3%.

Connected through hyperedge HE-1 (induced by source paper "on the limitations of analyzing worst case dynamic energy of processing"):
  with members:
    - "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"
    - "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 "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "the iot energy challenge a software perspective"
    - "entra whole systems energy transparency"
    - "identifying compiler options to minimize energy consumption for embedded platforms"
    - "static analysis of energy consumption for llvm ir programs"
    - "beebs open benchmarks for energy measurements on embedded platforms"

Connected through hyperedge HE-3 (induced by source paper "interval based resource usage verification by translation into horn clauses and an application to energy consumption"):
  with members:
    - "towards energy consumption verification via static analysis"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "resource usage analysis of logic programs via abstract interpretation using sized types"
    - "higher order symbolic execution via contracts"
    - "more precise yet efficient type inference for logic programs"

Connected through hyperedge HE-4 (induced by source paper "the iot energy challenge a software perspective"):
  with members:
    - "on the limitations of analyzing worst case dynamic energy of processing"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"

Connected through hyperedge HE-5 (induced by source paper "data dependent energy modeling for worst case energy consumption analysis"):
  with members:
    - "on the infeasibility of analysing worst case dynamic energy"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "static analysis of energy consumption for llvm ir programs"
    - "optimizing the flash ram energy trade off in deeply embedded systems"
    - "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.OH(Other Computer Science)