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: "making root cause analysis feasible for large code bases a solution approach for a climate model"
  Abstract: For large-scale simulation codes with huge and complex code bases, where bit-for-bit comparisons are too restrictive, finding the source of statistically significant discrepancies (e.g., from a previous version, alternative hardware or supporting software stack) in output is non-trivial at best. Although there are many tools for program comprehension through debugging or slicing, few (if any) scale to a model as large as the Community Earth System Model (CESM; trademarked), which consists of more than 1.5 million lines of Fortran code. Currently for the CESM, we can easily determine whether a discrepancy exists in the output using a by now well-established statistical consistency testing tool. However, this tool provides no information as to the possible cause of the detected discrepancy, leaving developers in a seemingly impossible (and frustrating) situation. Therefore, our aim in this work is to provide the tools to enable developers to trace a problem detected through the CESM output to its source. To this end, our strategy is to reduce the search space for the root cause(s) to a tractable size via a series of techniques that include creating a directed graph of internal CESM variables, extracting a subgraph (using a form of hybrid program slicing), partitioning into communities, and ranking nodes by centrality. Runtime variable sampling then becomes feasible in this reduced search space. We demonstrate the utility of this process on multiple examples of CESM simulation output by illustrating how sampling can be performed as part of an efficient parallel iterative refinement procedure to locate error sources, including sensitivity to CPU instructions. By providing CESM developers with tools to identify and understand the reason for statistically distinct output, we have positively impacted the CESM software development cycle and, in particular, its focus on quality assurance.

Connected through hyperedge HE-1 (induced by source paper "multi level analysis of compiler induced variability and performance tradeoffs"):
  with members:
    - "standing together for reproducibility in large scale computing report on reproducibility xsede"

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)