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: "beebs open benchmarks for energy measurements on embedded platforms"
  Abstract: This paper presents and justifies an open benchmark suite named BEEBS, targeted at evaluating the energy consumption of embedded processors. #R##N#We explore the possible sources of energy consumption, then select individual benchmarks from contemporary suites to cover these areas. Version one of BEEBS is presented here and contains 10 benchmarks that cover a wide range of typical embedded applications. The benchmark suite is portable across diverse architectures and is freely available. #R##N#The benchmark suite is extensively evaluated, and the properties of its constituent programs are analysed. Using real hardware platforms we show case examples which illustrate the difference in power dissipation between three processor architectures and their related ISAs. We observe significant differences in the average instruction dissipation between the architectures of 4.4x, specifically 170uW/MHz (ARM Cortex-M0), 65uW/MHz (Adapteva Epiphany) and 88uW/MHz (XMOS XS1-L1).

Connected through hyperedge HE-1 (induced by source paper "silhouette efficient protected shadow stacks on embedded systems"):
  with members:
    - "sponge based control flow protection for iot devices"
    - "control flow integrity precision security and performance"

Connected through hyperedge HE-2 (induced by source paper "benchpress analyzing android app vulnerability benchmark suites"):
  with members:
    - "are free android app security analysis tools effective in detecting known vulnerabilities"
    - "do android taint analysis tools keep their promises"
    - "ghera a repository of android app vulnerability benchmarks"
    - "horndroid practical and sound static analysis of android applications by smt solving"

Connected through hyperedge HE-3 (induced by source paper "silhouette efficient intra address space isolation for protected shadow stacks on embedded systems"):
  with members:
    - "control flow integrity precision security and performance"

Connected through hyperedge HE-4 (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"
    - "static analysis of energy consumption for llvm ir programs"

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"
    - "static analysis of energy consumption for llvm ir programs"
    - "optimizing the flash ram energy trade off in deeply embedded systems"

Connected through hyperedge HE-6 (induced by source paper "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"):
  with members:
    - "identifying compiler options to minimize energy consumption for embedded platforms"

Connected through hyperedge HE-7 (induced by source paper "parameter sensitivity analysis of the energy frequency convexity rule for nanometer scale application processors"):
  with members:
    - "modeling the temperature bias of power consumption for nanometer scale cpus in application processors"
    - "the energy frequency convexity rule modeling and experimental validation on mobile devices"
    - "exploring performance and power properties of modern multicore chips via simple machine models"
    - "some observations on optimal frequency selection in dvfs based energy consumption minimization"

Connected through hyperedge HE-8 (induced by source paper "data dependent energy modelling a worst case perspective"):
  with members:
    - "optimizing the flash ram energy trade off in deeply embedded systems"

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)