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: "transfer learning oriented class imbalance learning for cross project defect prediction"
  Abstract: Cross-project defect prediction (CPDP) aims to predict defects of projects lacking training data by using prediction models trained on historical defect data from other projects. However, since the distribution differences between datasets from different projects, it is still a challenge to build high-quality CPDP models. Unfortunately, class imbalanced nature of software defect datasets further increases the difficulty. In this paper, we propose a transferlearning oriented minority over-sampling technique (TOMO) based feature weighting transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPDP by considering both classimbalance and feature importance problems. Differing from traditional over-sampling techniques, TOMO not only can balance the data but reduce the distribution difference. And then FWTNB is used to further increase the similarity of two distributions. Experiments are performed on 11 public defect datasets. The experimental results show that (1) TOMO improves the average G-Measure by 23.7\%$\sim$41.8\%, and the average MCC by 54.2\%$\sim$77.8\%. (2) feature weighting (FW) strategy improves the average G-Measure by 11\%, and the average MCC by 29.2\%. (3) TOMOFWTNB improves the average G-Measure value by at least 27.8\%, and the average MCC value by at least 71.5\%, compared with existing state-of-theart CPDP approaches. It can be concluded that (1) TOMO is very effective for addressing class-imbalance problem in CPDP scenario; (2) our FW strategy is helpful for CPDP; (3) TOMOFWTNB outperforms previous state-of-the-art CPDP approaches.

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"
    - "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"
    - "an empirical study on software defect prediction with a simplified metric set"

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)