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: "an empirical study on software defect prediction with a simplified metric set"
  Abstract: ContextSoftware defect prediction plays a crucial role in estimating the most defect-prone components of software, and a large number of studies have pursued improving prediction accuracy within a project or across projects. However, the rules for making an appropriate decision between within- and cross-project defect prediction when available historical data are insufficient remain unclear. ObjectiveThe objective of this work is to validate the feasibility of the predictor built with a simplified metric set for software defect prediction in different scenarios, and to investigate practical guidelines for the choice of training data, classifier and metric subset of a given project. MethodFirst, based on six typical classifiers, three types of predictors using the size of software metric set were constructed in three scenarios. Then, we validated the acceptable performance of the predictor based on Top-k metrics in terms of statistical methods. Finally, we attempted to minimize the Top-k metric subset by removing redundant metrics, and we tested the stability of such a minimum metric subset with one-way ANOVA tests. ResultsThe study has been conducted on 34 releases of 10 open-source projects available at the PROMISE repository. The findings indicate that the predictors built with either Top-k metrics or the minimum metric subset can provide an acceptable result compared with benchmark predictors. The guideline for choosing a suitable simplified metric set in different scenarios is presented in Table 12. ConclusionThe experimental results indicate that (1) the choice of training data for defect prediction should depend on the specific requirement of accuracy; (2) the predictor built with a simplified metric set works well and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naive Bayes) also tend to perform well when using a simplified metric set for defect prediction; and (4) in several cases, the minimum metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.

Connected through hyperedge HE-1 (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"
    - "the impact of automated parameter optimization on defect prediction models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "why software projects need heroes lessons learned from 1100 projects"):
  with members:
    - "statistical analysis of metrics for software quality improvement"
    - "the impact of correlated metrics on defect models"
    - "coding together at scale github as a collaborative social network"
    - "comparative study on agile software development methodologies"

Connected through hyperedge HE-3 (induced by source paper "transfer learning oriented class imbalance learning for cross project defect prediction"):
  with members:
    - "transfer learning for speech and language processing"
    - "locally weighted naive bayes"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "revisiting heterogeneous defect prediction how far are we"):
  with members:
    - "bellwethers a baseline method for transfer learning"
    - "towards cross project defect prediction with imbalanced feature sets"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-5 (induced by source paper "why software projects need heroes lessons learned from 1000 projects"):
  with members:
    - "the impact of correlated metrics on defect models"
    - "comparative study on agile software development methodologies"

Connected through hyperedge HE-6 (induced by source paper "a systematic study of cross project defect prediction with meta learning"):
  with members:
    - "a unified view of multi label performance measures"

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)