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: "on the state and importance of reproducible experimental research in parallel computing"
  Abstract: Computer science is also an experimental science. This is particularly the case for parallel computing, which is in a total state of flux, and where experiments are necessary to substantiate, complement, and challenge theoretical modeling and analysis. Here, experimental work is as important as are advances in theory, that are indeed often driven by the experimental findings. In parallel computing, scientific contributions presented in research articles are therefore often based on experimental data, with a substantial part devoted to presenting and discussing the experimental findings. As in all of experimental science, experiments must be presented in a way that makes reproduction by other researchers possible, in principle. Despite appearance to the contrary, we contend that reproducibility plays a small role, and is typically not achieved. As can be found, articles often do not have a sufficiently detailed description of their experiments, and do not make available the software used to obtain the claimed results. As a consequence, parallel computational results are most often impossible to reproduce, often questionable, and therefore of little or no scientific value. We believe that the description of how to reproduce findings should play an important part in every serious, experiment-based parallel computing research article. We aim to initiate a discussion of the reproducibility issue in parallel computing, and elaborate on the importance of reproducible research for (1) better and sounder technical/scientific papers, (2) a sounder and more efficient review process and (3) more effective collective work. This paper expresses our current view on the subject and should be read as a position statement for discussion and future work. We do not consider the related (but no less important) issue of the quality of the experimental design.

Connected through hyperedge HE-1 (induced by source paper "performance reproduction and prediction of selected dynamic loop scheduling experiments"):
  with members:
    - "experimental verification and analysis of dynamic loop scheduling in scientific applications"
    - "efficient generation of parallel spin images using dynamic loop scheduling"
    - "exploring the relation between two levels of scheduling using a novel simulation approach"

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)