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: "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.

Connected through hyperedge HE-1 (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 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 "entra whole systems energy transparency"):
  with members:
    - "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"
    - "modeling and visualizing networked multi core embedded software energy consumption"

Connected through hyperedge HE-3 (induced by source paper "inferring energy bounds via static program analysis and evolutionary modeling of basic blocks"):
  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"

Connected through hyperedge HE-4 (induced by source paper "inferring energy bounds statically by evolutionary analysis of basic blocks"):
  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"

Connected through hyperedge HE-5 (induced by source paper "inferring parametric energy consumption functions at different software levels isa vs llvm ir"):
  with members:
    - "static analysis of energy consumption for llvm ir programs"
    - "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.PF(Performance)