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: "better data is better than better data miners 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-criteria tests while (b) fixing the weaker regions of the training data (using SMOTUNED, which is a self-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 were independent of the classifier used to predict for quality. #R##N#We hence conclude that, for software analytics, (1) data pre-processing can be more important than classifier choice, (2) ranking studies are incomplete without pre-processing and (3) SMOTUNED is a promising candidate for pre-processing.

Connected through hyperedge HE-1 (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"
    - "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-2 (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"
    - "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-3 (induced by source paper "learning general principles from hundreds of software projects"):
  with members:
    - "bellwethers a baseline method for transfer learning"
    - "are delayed issues harder to resolve revisiting cost to fix of defects throughout the lifecycle"
    - "better predictors for issue lifetime"
    - "a dissection of the test driven development process does it really matter to test first or to test last"
    - "defining and conceptualizing actionable insight a conceptual framework for decision centric analytics"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (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"
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "is one hyperparameter optimizer enough"):
  with members:
    - "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"
    - "can you explain that better comprehensible text analytics for se applications"
    - "building better quality predictors using epsilon dominance"
    - "bellwethers a baseline method for transfer learning"
    - "riot a novel stochastic method for rapidly configuring cloud based workflows"
    - "easy over hard a case study on deep learning"

Connected through hyperedge HE-6 (induced by source paper "bellwethers a baseline method for transfer learning"):
  with members:
    - "less is more minimizing code reorganization using xtree"
    - "better predictors for issue lifetime"
    - "negative results for software effort estimation"
    - "tuning for software analytics"
    - "smote synthetic minority over sampling technique"

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)