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 modelling a worst case perspective"
  Abstract: Energy consumption of the software running on a device has become increasingly important as a growing number of devices rely on batteries or other limited sources of power. Of particular interest is constructing a bounded measure of the energy consumption - the maximum energy a program could consume for any input given to it. #R##N#We explore the effect of different data on the energy consumption of individual instructions, instruction sequences and full programs. The whole program energy consumption of two benchmarks is analysed over random and hand-crafted data, and maximized with genetic algorithms for two embedded processors. We find that the worst case can be predicted from the distribution created by the random data, however, hand-crafted data can often achieve lower energy consumption. #R##N#A model is constructed that allows the worst case energy for a sequence of instructions to be predicted. This is based on the observation that the transition between instructions is important and thus is not a single energy cost - it is a distribution dependent on the input and output values of the two consecutive instructions. We characterise the transition distributions for several instructions in the AVR instruction set, and show that this gives a useful upper bound on the energy consumption. We explore the effect that the transfer function of the instruction has on the data, and give an example which leads to a bimodal energy distribution. Finally, we conclude that a probabilistic approach is appropriate for estimating the energy consumption of programs.

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 modeling for worst case energy consumption analysis"
    - "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 "energy transparency for deeply embedded programs"):
  with members:
    - "entra whole systems energy transparency"
    - "a high level model of embedded flash energy consumption"
    - "static analysis of energy consumption for llvm ir programs"
    - "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-3 (induced by source paper "on the infeasibility of analysing worst case dynamic energy"):
  with members:
    - "on the value and limits of multi level energy consumption static analysis for deeply embedded single and multi threaded programs"
    - "static analysis of energy consumption for llvm ir programs"

Connected through hyperedge HE-4 (induced by source paper "an energy aware programming approach for mobile application development guided by a fine grained energy model"):
  with members:
    - "a top to bottom view energy analysis for mobile application source code"

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)