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: "tuning for software analytics"
  Abstract: Context: Data miners have been widely used in software engineering to, say, generate defect predictors from static code measures. Such static code defect predictors perform well compared to manual methods, and they are easy to use and useful to use. But one of the "black arts" of data mining is setting the tunings that control the miner.Objective: We seek simple, automatic, and very effective method for finding those tunings.Method: For each experiment with different data sets (from open source JAVA systems), we ran differential evolution as an optimizer to explore the tuning space (as a first step) then tested the tunings using hold-out data.Results: Contrary to our prior expectations, we found these tunings were remarkably simple: it only required tens, not thousands, of attempts to obtain very good results. For example, when learning software defect predictors, this method can quickly find tunings that alter detection precision from 0% to 60%.Conclusion: Since (1)?the improvements are so large, and (2)?the tuning is so simple, we need to change standard methods in software analytics. At least for defect prediction, it is no longer enough to just run a data miner and present the result without conducting a tuning optimization study. The implication for other kinds of analytics is now an open and pressing issue.

Connected through hyperedge HE-1 (induced by source paper "how to improve ai tools by adding in se knowledge experiments with the timelime defect reduction tool"):
  with members:
    - "learning actionable analytics from multiple software projects"
    - "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"
    - "applications of psychological science for actionable analytics"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "why should i trust you explaining the predictions of any classifier"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "whence to learn transferring knowledge in configurable systems using beetle"):
  with members:
    - "finding faster configurations using flash"
    - "micky a cheaper alternative for selecting cloud instances"
    - "scout an experienced guide to find the best cloud configuration"
    - "transfer learning for improving model predictions in highly configurable software"
    - "bellwethers a baseline method for transfer learning"
    - "using bad learners to find good configurations"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "faster discovery of faster system configurations with spectral learning"

Connected through hyperedge HE-3 (induced by source paper "how to dodge complex software analytics"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "the impact of automated parameter optimization on defect prediction models"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"

Connected through hyperedge HE-4 (induced by source paper "is ai different for se"):
  with members:
    - "how to dodge complex software analytics"
    - "a benchmark study on sentiment analysis for software engineering research"
    - "applications of psychological science for actionable analytics"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "bellwethers a baseline method for transfer learning"
    - "less is more minimizing code reorganization using xtree"

Connected through hyperedge HE-5 (induced by source paper "enhanced labeling of issue reports with f 3 t"):
  with members:
    - "how to dodge complex software analytics"
    - "replication can improve prior results a github study of pull request acceptance"
    - "applications of psychological science for actionable analytics"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "finding better active learners for faster literature reviews"
    - "we don t need another hero the impact of heroes on software development"
    - "bellwethers a baseline method for transfer learning"
    - "easy over hard a case study on deep learning"

Connected through hyperedge HE-6 (induced by source paper "better security bug report classification via hyperparameter optimization"):
  with members:
    - "bad smells in software analytics papers"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "less is more minimizing code reorganization using xtree"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "why is differential evolution better than grid search for tuning defect predictors"

Connected through hyperedge HE-7 (induced by source paper "evaluating software defect prediction performance an updated benchmarking study"):
  with members:
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "easy over hard a case study on deep learning"
    - "statistical comparison of classifiers through bayesian hierarchical modelling"
    - "should we really use post hoc tests based on mean ranks"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-8 (induced by source paper "predicting performance of software configurations there is no silver bullet"):
  with members:
    - "finding faster configurations using flash"
    - "using bad learners to find good configurations"
    - "easy over hard a case study on deep learning"
    - "faster discovery of faster system configurations with spectral learning"
    - "a comparison of 10 sampling algorithms for configurable systems"
    - "distribution aware sampling and weighted model counting for sat"

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)