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: "bayesian data analysis in empirical software engineering research"
  Abstract: Statistics comes in two main flavors: frequentist and Bayesian. For historical and technical reasons, frequentist statistics have traditionally dominated empirical data analysis, and certainly remain prevalent in empirical software engineering. This situation is unfortunate because frequentist statistics suffer from a number of shortcomings---such as lack of flexibility and results that are unintuitive and hard to interpret---that curtail their effectiveness when dealing with the heterogeneous data that is increasingly available for empirical analysis of software engineering practice. #R##N#In this paper, we pinpoint these shortcomings, and present Bayesian data analysis techniques that work better on the same data---as they can provide clearer results that are simultaneously robust and nuanced. After a short, high-level introduction to the basic tools of Bayesian statistics, our presentation targets the reanalysis of two empirical studies targeting data about the effectiveness of automatically generated tests and the performance of programming languages. By contrasting the original frequentist analysis to our new Bayesian analysis, we demonstrate concrete advantages of using Bayesian techniques, and we advocate a prominent role for them in empirical software engineering research and practice.

Connected through hyperedge HE-1 (induced by source paper "the unfulfilled potential of data driven decision making in agile software development"):
  with members:
    - "arguing practical significance in software engineering using bayesian data analysis"
    - "ways of applying artificial intelligence in software engineering"
    - "is it possible to disregard obsolete requirements an initial experiment on a potentially new bias in software effort estimation"

Connected through hyperedge HE-2 (induced by source paper "evolution of statistical analysis in empirical software engineering research current state and steps forward"):
  with members:
    - "four commentaries on the use of students and professionals in empirical software engineering experiments"
    - "bayesian hierarchical modelling for tailoring metric thresholds"
    - "arguing practical significance in software engineering using bayesian data analysis"
    - "useful statistical methods for human factors research in software engineering a discussion on validation with quantitative 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.SE(Software Engineering)