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: "compiler phase ordering as an orthogonal approach for reducing energy consumption"
  Abstract: Compiler writers typically focus primarily on the performance of the generated program binaries when selecting the passes and the order in which they are applied in the standard optimization levels, such as GCC -O3. In some domains, such as embedded systems and High-Performance Computing (HPC), it might be sometimes acceptable to slowdown computations if the energy consumed can be significantly decreased. Embedded systems often rely on a battery and besides energy also have power dissipation limitations, while HPC centers have a growing concern with electricity and cooling costs. Relying on power policies to apply frequency/voltage scaling and/or change the CPU to idle states (e.g., alternate between power levels in bursts) as the main method to reduce energy leaves potential for improvement using other orthogonal approaches. In this work we evaluate the impact of compiler pass sequences specialization (also known as compiler phase ordering) as a means to reduce the energy consumed by a set of programs/functions when comparing with the use of the standard compiler phase orders provided by, e.g., -OX flags. We use our phase selection and ordering framework to explore the design space in the context of a Clang+LLVM compiler targeting a multicore ARM processor in an ODROID board and a dual x86 desktop representative of a node in a Supercomputing center. Our experiments with a set of representative kernels show that there we can reduce energy consumption by up to 24% and that some of these improvements can only be partially explained by improvements to execution time. The experiments show cases where applications that run faster consume more energy. Additionally, we make an effort to characterize the compiler sequence exploration space in terms of their impact on performance and energy.

Connected through hyperedge HE-1 (induced by source paper "a survey on compiler autotuning using machine learning"):
  with members:
    - "less is more exploiting the standard compiler optimization levels for better performance and 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"

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)