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: "static analysis of energy consumption for llvm ir programs"
  Abstract: Energy models can be constructed by characterizing the energy consumed by executing each instruction in a processor's instruction set. This can be used to determine how much energy is required to execute a sequence of assembly instructions, without the need to instrument or measure hardware. #R##N#However, statically analyzing low-level program structures is hard, and the gap between the high-level program structure and the low-level energy models needs to be bridged. We have developed techniques for performing a static analysis on the intermediate compiler representations of a program. Specifically, we target LLVM IR, a representation used by modern compilers, including Clang. Using these techniques we can automatically infer an estimate of the energy consumed when running a function under different platforms, using different compilers. #R##N#One of the challenges in doing so is that of determining an energy cost of executing LLVM IR program segments, for which we have developed two different approaches. When this information is used in conjunction with our analysis, we are able to infer energy formulae that characterize the energy consumption for a particular program. This approach can be applied to any languages targeting the LLVM toolchain, including C and XC or architectures such as ARM Cortex-M or XMOS xCORE, with a focus towards embedded platforms. Our techniques are validated on these platforms by comparing the static analysis results to the physical measurements taken from the hardware. Static energy consumption estimation enables energy-aware software development, without requiring hardware knowledge.

Connected through hyperedge HE-1 (induced by source paper "resource analysis driven by conditional termination proofs"):
  with members:
    - "an iterative approach to precondition inference using constrained horn clauses"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "a simple and scalable static analysis for bound analysis and amortized complexity analysis"

Connected through hyperedge HE-2 (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"
    - "a high level model of embedded flash energy consumption"

Connected through hyperedge HE-3 (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"
    - "energy transparency for deeply embedded programs"
    - "entra whole systems energy transparency"
    - "identifying compiler options to minimize energy consumption for embedded platforms"
    - "beebs open benchmarks for energy measurements on embedded platforms"

Connected through hyperedge HE-4 (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"
    - "a high level model of embedded flash energy consumption"
    - "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-5 (induced by source paper "data dependent energy modeling for worst case energy consumption analysis"):
  with members:
    - "energy transparency for deeply embedded programs"
    - "on the infeasibility of analysing worst case dynamic energy"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"
    - "optimizing the flash ram energy trade off in deeply embedded systems"
    - "beebs open benchmarks for energy measurements on embedded platforms"

Connected through hyperedge HE-6 (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"
    - "on the value and limits of multi level energy consumption static analysis for deeply embedded single and multi threaded programs"
    - "probabilistic output analysis by program manipulation"
    - "inferring parametric energy consumption functions at different software levels isa vs llvm ir"

Connected through hyperedge HE-7 (induced by source paper "on the infeasibility of analysing worst case dynamic energy"):
  with members:
    - "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"

Connected through hyperedge HE-8 (induced by source paper "inferring parametric energy consumption functions at different software levels isa vs llvm ir"):
  with members:
    - "data dependent energy modeling for worst case energy consumption analysis"
    - "resource usage analysis of logic programs via abstract interpretation using sized types"

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)