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: "on the infeasibility of analysing worst case dynamic energy"
  Abstract: In this paper we study the sources of dynamic energy during the execution of software on microprocessors suited for the Internet of Things (IoT) domain. Estimating the energy consumed by executing software is typically achieved by determining the most costly path through the program according to some energy model of the processor. Few models, however, adequately tackle the matter of dynamic energy caused by operand data. We find that the contribution of operand data to overall energy can be significant, prove that finding the worst-case input data is NP-hard, and further, that it cannot be estimated to any useful factor. Our work shows that accurate worst-case analysis of data dependent energy is infeasible, and that other techniques for energy estimation should be considered.

Connected through hyperedge HE-1 (induced by source paper "data dependent energy modeling for worst case energy consumption analysis"):
  with members:
    - "energy transparency for deeply embedded programs"
    - "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"
    - "beebs open benchmarks for energy measurements on embedded platforms"

Connected through hyperedge HE-2 (induced by source paper "entra whole systems energy transparency"):
  with members:
    - "data dependent energy modeling for worst case energy consumption analysis"
    - "inferring energy bounds statically by evolutionary analysis of basic blocks"
    - "probabilistic resource analysis by program transformation"
    - "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"

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.CC(Computational Complexity)