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 "on the limitations of analyzing worst case dynamic energy of processing"):
  Abstract: This article examines dynamic energy consumption caused by data during software execution on deeply embedded microprocessors, which can be significant on some devices. In worst-case energy consumption analysis, energy models are used to find the most costly execution path. Taking each instruction’s worst-case energy produces a safe but overly pessimistic upper bound. Algorithms for safe and tight bounds would be desirable. We show that finding exact worst-case energy is NP-hard, and that tight bounds cannot be approximated with guaranteed safety. We conclude that any energy model targeting tightness must either sacrifice safety or accept overapproximation proportional to data-dependent energy.

Includes members:
  Member "energy transparency for deeply embedded programs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "less is more exploiting the standard compiler optimization levels for better performance and energy consumption")
  Member "data dependent energy modeling for worst case energy consumption analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entra whole systems energy transparency")
  Member "data dependent energy modelling a worst case perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy transparency for deeply embedded programs")
  Member "on the value and limits of multi level energy consumption static analysis for deeply embedded single and multi threaded programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "resource analysis driven by conditional termination proofs")
  Member "a high level model of embedded flash energy consumption":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "energy transparency for deeply embedded programs")
  Member "static analysis of energy consumption for llvm ir programs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "resource analysis driven by conditional termination proofs")

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.CC(Computational Complexity)