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: "identifying compiler options to minimize energy consumption for embedded platforms"
  Abstract: Thispaperpresentsananalysisoftheenergyconsumptionofanextensivenumberoftheoptimizations a modern compiler can perform. Using GCC as a test case, we evaluate a set of 10 carefully selected benchmarksfor5differentembeddedplatforms.Afractionalfactorialdesignisusedtosystematically explore the large optimization space (2 82 possible combinations), while still accurately determining the effects of optimizations and optimization combinations. Hardware power measurements on each platform are taken to ensure all architectural effects on the energy consumption are captured. We show that fractional factorial design can find more optimal combinations than relying on built-in compilersettings.Weexploretherelationshipbetweenrun-timeandenergyconsumption,andidentify scenarios where they are and are not correlated. A further conclusion of this study is the structure of the benchmark has a larger effect than the hardware architecture on whether the optimization will be effective, and that no single optimization is universally beneficial for execution time or energy consumption.

Connected through hyperedge HE-1 (induced by source paper "a survey on compiler autotuning using machine learning"):
  with members:
    - "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"
    - "compiler phase ordering as an orthogonal approach for reducing energy consumption"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"
    - "collective mind part ii towards performance and cost aware software engineering as a natural science"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "autotuning opencl workgroup size for stencil patterns"
    - "iterative compilation on mobile devices"

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"
    - "energy transparency for deeply embedded programs"
    - "entra whole systems energy transparency"
    - "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 "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"):
  with members:
    - "beebs open benchmarks for energy measurements on embedded platforms"

Connected through hyperedge HE-4 (induced by source paper "static analysis of energy consumption for llvm ir programs"):
  with members:
    - "beebs open benchmarks for energy measurements on embedded platforms"
    - "bound analysis of imperative programs with the size change abstraction extended version"

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.PF(Performance)