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: "is better data better than better data miners on the benefits of tuning smote for defect prediction"
  Abstract: We report and fix an important systematic error in prior studies that ranked classifiers for software analytics. Those studies did not (a) assess classifiers on multiple criteria and they did not (b) study how variations in the data affect the results. Hence, this paper applies (a) multi-performance criteria while (b) fixing the weaker regions of the training data (using SMOTUNED, which is an auto-tuning version of SMOTE). This approach leads to dramatically large increases in software defect predictions when applied in a 5*5 cross-validation study for 3,681 JAVA classes (containing over a million lines of code) from open source systems, SMOTUNED increased AUC and recall by 60% and 20% respectively. These improvements are independent of the classifier used to predict for defects. Same kind of pattern (improvement) was observed when a comparative analysis of SMOTE and SMOTUNED was done against the most recent class imbalance technique.   In conclusion, for software analytic tasks like defect prediction, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without such pre-processing, and (3) SMOTUNED is a promising candidate for pre-processing.

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"
    - "why should i trust you explaining the predictions of any classifier"
    - "tuning for software analytics"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (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"
    - "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"
    - "predicting good configurations for github and stack overflow topic models"

Connected through hyperedge HE-3 (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"
    - "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"
    - "better predictors for issue lifetime"

Connected through hyperedge HE-4 (induced by source paper "better security bug report classification via hyperparameter optimization"):
  with members:
    - "bad smells in software analytics papers"
    - "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"
    - "tuning for software analytics"

Connected through hyperedge HE-5 (induced by source paper "evaluating software defect prediction performance an updated benchmarking study"):
  with members:
    - "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"
    - "tuning for software analytics"
    - "should we really use post hoc tests based on mean ranks"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-6 (induced by source paper "better software analytics via duo data mining algorithms using used by optimizers"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "data driven search based software engineering"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"
    - "an improved generic bet and run strategy for speeding up stochastic local search"
    - "predicting good configurations for github and stack overflow topic models"

Connected through hyperedge HE-7 (induced by source paper "data driven search based software engineering"):
  with members:
    - "finding faster configurations using flash"
    - "the impact of automated parameter optimization on defect prediction models"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "finding better active learners for faster literature reviews"
    - "transfer learning for improving model predictions in highly configurable software"
    - "short er reasoning about larger requirements models"
    - "using bad learners to find good configurations"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"

Connected through hyperedge HE-8 (induced by source paper "bad smells in software analytics papers"):
  with members:
    - "the impact of automated parameter optimization on defect prediction models"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "data driven search based software engineering"
    - "we don t need another hero the impact of heroes on software development"
    - "revisiting unsupervised learning for defect prediction"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "easy over hard a case study on deep learning"
    - "sampling as a baseline optimizer for search based software engineering"

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)