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: "mcompiler a synergistic compilation framework"
  Abstract: This paper presents a meta-compilation framework, the MCompiler. The main idea is that different segments of a program can be compiled with different compilers/optimizers and combined into a single executable. The MCompiler can be used in a number of ways. It can generate an executable with higher performance than any individual compiler, because each compiler uses a specific, ordered set of optimization techniques and different profitability models and can, therefore, generate code significantly different from other compilers. Alternatively, the MCompiler can be used by researchers and compiler developers to evaluate their compiler implementation and compare it to results from other available compilers/optimizers. #R##N#A code segment in this work is a loop nest, but other choices are possible. This work also investigates the use of Machine Learning to learn inherent characteristics of loop nests and then predict during compilation the most suited code optimizer for each loop nest in an application. This reduces the need for profiling applications as well as the compilation time. #R##N#The results show that our framework improves the overall performance for applications over state-of-the-art compilers by a geometric mean of 1.96x for auto-vectorized code and 2.62x for auto-parallelized code. Parallel applications with OpenMP directives are also improved by the MCompiler, with a geometric mean performance improvement of 1.04x (up to 1.74x). The use of Machine Learning prediction achieves performance very close to the profiling-based search for choosing the most suited code optimizer: within 4% for auto-vectorized code and within 8% for auto-parallelized code. Finally, the MCompiler can be expanded to collect metrics other than performance to be used in optimization process. The example presented is collecting energy data.

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.DC(Distributed, Parallel, and Cluster Computing)