human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "data dependent energy modeling for worst case energy consumption analysis"):
  Abstract: Safely meeting Worst Case Energy Consumption (WCEC) criteria requires accurate energy modeling of software. We investigate the impact of instruction operand values upon energy consumption in cacheless embedded processors. Existing instruction-level energy models typically use measurements from random input data, providing estimates unsuitable for safe WCEC analysis.   We examine probabilistic energy distributions of instructions and propose a model for composing instruction sequences using distributions, enabling WCEC analysis on program basic blocks. The worst case is predicted with statistical analysis. Further, we verify that the energy of embedded benchmarks can be characterised as a distribution, and compare our proposed technique with other methods of estimating energy consumption.

Includes members:
  Member "energy transparency for deeply embedded programs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the limitations of analyzing worst case dynamic energy of processing")
  Member "on the infeasibility of analysing worst case dynamic energy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entra whole systems energy transparency")
  Member "inferring parametric energy consumption functions at different software levels isa vs llvm ir":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource analysis driven by conditional termination proofs")
  Member "static analysis of energy consumption for llvm ir programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "resource analysis driven by conditional termination proofs")
  Member "optimizing the flash ram energy trade off in deeply embedded systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy transparency for deeply embedded programs")
  Member "beebs open benchmarks for energy measurements on embedded platforms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "silhouette efficient protected shadow stacks on embedded systems")

Question: Please tell me which class the source paper 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)