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: "iterative compilation on mobile devices"
  Abstract: The abundance of poorly optimized mobile applications coupled with their increasing centrality in our digital lives make a framework for mobile app optimization an imperative. While tuning strategies for desktop and server applications have a long history, it is difficult to adapt them for use on mobile phones. #R##N#Reference inputs which trigger behavior similar to a mobile application's typical are hard to construct. For many classes of applications the very concept of typical behavior is nonexistent, each user interacting with the application in very different ways. In contexts like this, optimization strategies need to evaluate their effectiveness against real user input, but doing so online runs the risk of user dissatisfaction when suboptimal optimizations are evaluated. #R##N#In this paper we present an iterative compiler which employs a novel capture and replay technique in order to collect real user input and use it later to evaluate different transformations offline. The proposed mechanism identifies and stores only the set of memory pages needed to replay the most heavily used functions of the application. At idle periods, this minimal state is combined with different binaries of the application, each one build with different optimizations enabled. Replaying the targeted functions allows us to evaluate the effectiveness of each set of optimizations for the actual way the user interacts with the application. #R##N#For the BEEBS benchmark suite, our approach was able to improve performance by up to 57%, while keeping the slowdown experienced by the user on average at 0.8%. By focusing only on heavily used functions, we are able to conserve storage space by between two and three orders of magnitude compared to typical capture and replay implementations.

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"
    - "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"
    - "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.PL(Programming Languages)