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: "experimental verification and analysis of dynamic loop scheduling in scientific applications"
  Abstract: Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. Dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of How realistic are the simulations of executions of scientific applications using DLS on HPC platforms? In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC plat- forms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy, and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

Connected through hyperedge HE-1 (induced by source paper "an approach for realistically simulating the performance of scientific applications on high performance computing systems"):
  with members:
    - "openmp loop scheduling revisited making a case for more schedules"
    - "sil an approach for adjusting applications to heterogeneous systems under perturbations"
    - "performance reproduction and prediction of selected dynamic loop scheduling experiments"
    - "efficient generation of parallel spin images using dynamic loop scheduling"
    - "exploring the relation between two levels of scheduling using a novel simulation approach"

Connected through hyperedge HE-2 (induced by source paper "simas a simulation assisted approach for the scheduling algorithm selection under perturbations"):
  with members:
    - "an approach for realistically simulating the performance of scientific applications on high performance computing systems"
    - "performance reproduction and prediction of selected dynamic loop scheduling experiments"
    - "the importance and need for system monitoring and analysis in hpc operations and research"
    - "efficient generation of parallel spin images using dynamic loop scheduling"

Connected through hyperedge HE-3 (induced by source paper "sil an approach for adjusting applications to heterogeneous systems under perturbations"):
  with members:
    - "performance reproduction and prediction of selected dynamic loop scheduling experiments"
    - "the importance and need for system monitoring and analysis in hpc operations and research"
    - "efficient generation of parallel spin images using dynamic loop scheduling"

Connected through hyperedge HE-4 (induced by source paper "performance reproduction and prediction of selected dynamic loop scheduling experiments"):
  with members:
    - "efficient generation of parallel spin images using dynamic loop scheduling"
    - "exploring the relation between two levels of scheduling using a novel simulation approach"
    - "on the state and importance of reproducible experimental research in parallel computing"

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)