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: "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"
  Abstract: This paper presents the interesting observation that by performing fewer of the optimizations available in a standard compiler optimization level such as -02, while preserving their original ordering, significant savings can be achieved in both execution time and energy consumption. This observation has been validated on two embedded processors, namely the ARM Cortex-M0 and the ARM Cortex-M3, using two different versions of the LLVM compilation framework; v3.8 and v5.0. Experimental evaluation with 71 embedded benchmarks demonstrated performance gains for at least half of the benchmarks for both processors. An average execution time reduction of 2.4% and 5.3% was achieved across all the benchmarks for the Cortex-M0 and Cortex-M3 processors, respectively, with execution time improvements ranging from 1% up to 90% over the -02. The savings that can be achieved are in the same range as what can be achieved by the state-of-the-art compilation approaches that use iterative compilation or machine learning to select flags or to determine phase orderings that result in more efficient code. In contrast to these time consuming and expensive to apply techniques, our approach only needs to test a limited number of optimization configurations, less than 64, to obtain similar or even better savings. Furthermore, our approach can support multi-criteria optimization as it targets execution time, energy consumption and code size at the same time.

Connected through hyperedge HE-1 (induced by source paper "a survey on compiler autotuning using machine learning"):
  with members:
    - "compiler phase ordering as an orthogonal approach for reducing energy consumption"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "automatically tuning the gcc compiler to optimize the performance of applications running on embedded systems"
    - "collective mind part ii towards performance and cost aware software engineering as a natural science"
    - "machine learning based auto tuning for enhanced opencl performance portability"
    - "autotuning opencl workgroup size for stencil patterns"
    - "iterative compilation on mobile devices"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "lost in translation exposing hidden compiler optimization opportunities"):
  with members:
    - "a survey on compiler autotuning using machine learning"
    - "the iot energy challenge a software perspective"
    - "a collective knowledge workflow for collaborative research into multi objective autotuning and machine learning techniques"
    - "entra whole systems energy transparency"

Connected through hyperedge HE-3 (induced by source paper "momit porting a javascript interpreter on a quarter coin"):
  with members:
    - "sampling as a baseline optimizer for search based software engineering"

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)