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: "data of low quality is better than no data"
  Abstract: Missing data is not uncommon in empirical software engineering research but a common way to handle it is to remove data completely. We believe that this is wasteful and should not be done out of habit. This paper aims to present a typical case in empirical software engineering research: Analyzing data, consisting of missingness, which has been collected and classified by others. By transferring empirical analysis methods from other disciplines, we here introduce the reader to approaches suitable for analyzing empirical software engineering data. We present a case study where we contrast with previous studies' methodologies (in effort estimation). Using principled Bayesian data analysis, together with state of art imputation techniques, we show how missing data and Bayesian data analysis can be considered a good match. The results show that by using low-quality data, instead of throwing data away, we still gain a better understanding of the resulting analysis if we are prepared to embrace uncertainty. Inferences can become weaker but, we argue, this is how it should be. Empirical software engineering research should make use of more state of art missing data techniques, not throw data away, and lean towards Bayesian data analysis in order to get a more nuanced view of the challenges we investigate.

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)