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 hierarchical modelling for tailoring metric thresholds"
  Abstract: Software is highly contextual. While there are cross-cutting 'global' lessons, individual software projects exhibit many 'local' properties. This data heterogeneity makes drawing local conclusions from global data dangerous. A key research challenge is to construct locally accurate prediction models that are informed by global characteristics and data volumes. Previous work has tackled this problem using clustering and transfer learning approaches, which identify locally similar characteristics. This paper applies a simpler approach known as Bayesian hierarchical modeling. We show that hierarchical modeling supports cross-project comparisons, while preserving local context. To demonstrate the approach, we conduct a conceptual replication of an existing study on setting software metrics thresholds. Our emerging results show our hierarchical model reduces model prediction error compared to a global approach by up to 50%.

Connected through hyperedge HE-1 (induced by source paper "bayesian data analysis in empirical software engineering research"):
  with members:
    - "classifying developers into core and peripheral an empirical study on count and network metrics"
    - "bayesian statistics in software engineering practical guide and case studies"
    - "a comparative study of programming languages in rosetta code"

Connected through hyperedge HE-2 (induced by source paper "arguing practical significance in software engineering using bayesian data analysis"):
  with members:
    - "evolution of statistical analysis in empirical software engineering research current state and steps forward"
    - "bayesian statistics in software engineering practical guide and case studies"

Connected through hyperedge HE-3 (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"
    - "arguing practical significance in software engineering using bayesian data analysis"
    - "bayesian data analysis in empirical software engineering research"
    - "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)