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: "case studies and challenges in reproducibility in the computational sciences"
  Abstract: This paper investigates the reproducibility of computational science research and identifies key challenges facing the community today. It is the result of the First Summer School o nE xperimental Methodology in Computational Science Research. First, we consider how to reproduce experiments that involv eh uman subjects, and in particular how to deal with dif ferentethics requirements at different institutions. Second, we look at whether parallel and distributed computational experiment sa re more or less reproducible than serial ones. Third, we consider reproducible computational experiments from fields outside computer science. Our final case study looks at whether reproducibility for one researcher is the same as for another, by having an author attempt to have others reproduce their own, reproducible, paper. This paper is open, executable and reproducible :t he whole process of writing this paper is captured in the source control repository hosting both the source of the paper, supplementary codes and data; we are providing setup for several experiments on which we were working; finally, we try to describe what we have achieved during the week of the school in a way that other sm ay reproduce (and hopefully improve) our experiments. ✦ 1I ntroduction In this paper we consider reproducibility in the com- putational sciences. We interpret the computational sciences as including computer science, but also any other science in which computational work plays an im- portant role, such as physics, biology or psychology. The central hypothesis of this paper is that reproducibility is a cornerstone of the scientific method, and should there- fore be a cornerstone of the computational sciences. We ask to what extent this is true and what challenges arise in reproducing work in the computational sciences. To study these questions, we perform four case stud- ies that consider different issues related to reproducibil- ity in the computational sciences. Each case study takes on a different aspect of reproducibility, identifies any problems encountered, and discusses the points that they have raised. An often-overlooked fact is that reproducibility in the computational sciences is not only an issue of the actual computations, but also of preliminaries and other setup that is not related to computation at all. The first case study asks how reproducibility is affected by differing ethics requirements at different universities.

Connected through hyperedge HE-1 (induced by source paper "reproducibility as a technical specification"):
  with members:
    - "towards reproducibility as a service"
    - "top tips to make your research irreproducible"
    - "community driven reviewing and validation of publications"
    - "can i implement your algorithm a model for reproducible research software"
    - "share and enjoy publishing useful and usable scientific models"
    - "best practices for scientific computing"
    - "the recomputation manifesto"

Connected through hyperedge HE-2 (induced by source paper "dear cav we need to talk about reproducibility"):
  with members:
    - "community driven reviewing and validation of publications"
    - "can i implement your algorithm a model for reproducible research software"
    - "share and enjoy publishing useful and usable scientific models"

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.CE(Computational Engineering, Finance, and Science)