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: "the impact of automated parameter optimization on defect prediction models"
  Abstract: Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28% of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.

Connected through hyperedge HE-1 (induced by source paper "puminer mining security posts from developer question and answer websites with pu learning"):
  with members:
    - "a look at the time delays in cvss vulnerability scoring"
    - "toxic code snippets on stack overflow"
    - "enhancing python compiler error messages via stack overflow"
    - "learning from positive and unlabeled data a survey"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "predicting good configurations for github and stack overflow topic models"
    - "xgboost a scalable tree boosting system"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "understanding the automated parameter optimization on transfer learning for cpdp an empirical study"):
  with members:
    - "which surrogate works for empirical performance modelling a case study with differential evolution"
    - "visualisation of pareto front approximation a short survey and empirical comparisons"
    - "how to dodge complex software analytics"
    - "transfer learning oriented class imbalance learning for cross project defect prediction"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "a systematic mapping study on cross project defect prediction"
    - "evolutionary many objective optimization based on adversarial decomposition"
    - "an empirical study on software defect prediction with a simplified metric set"

Connected through hyperedge HE-3 (induced by source paper "recognizing developers emotions while programming"):
  with members:
    - "a replication study on code comprehension and expertise using lightweight biometric sensors"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "whom are you going to call determinants of mentions in github discussions"
    - "emotion detection using noninvasive low cost sensors"
    - "mining valence arousal and dominance possibilities for detecting burnout and productivity"
    - "deep feelings a massive cross lingual study on the relation between emotions and virality"

Connected through hyperedge HE-4 (induced by source paper "how to dodge complex software analytics"):
  with members:
    - "finding faster configurations using flash"
    - "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"
    - "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-5 (induced by source paper "a replication study on code comprehension and expertise using lightweight biometric sensors"):
  with members:
    - "machine learning in automated text categorization"

Connected through hyperedge HE-6 (induced by source paper "on the time based conclusion stability of software defect prediction models"):
  with members:
    - "bellwethers a baseline method for transfer learning"

Connected through hyperedge HE-7 (induced by source paper "data driven search based software engineering"):
  with members:
    - "finding faster configurations using flash"
    - "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"
    - "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:
    - "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"
    - "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)