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: "neural network models for software development effort estimation a comparative study"
  Abstract: Software development effort estimation (SDEE) is one of the main tasks in software project management. It is crucial for a project manager to efficiently predict the effort or cost of a software project in a bidding process, since overestimation will lead to bidding loss and underestimation will cause the company to lose money. Several SDEE models exist; machine learning models, especially neural network models, are among the most prominent in the field. In this study, four different neural network models: Multilayer Perceptron, General Regression Neural Network, Radial Basis Function Neural Network, and Cascade Correlation Neural Network are compared with each other based on: (1) predictive accuracy centered on the Mean Absolute Error criterion, (2) whether such a model tends to overestimate or underestimate, and (3) how each model classifies the importance of its inputs. Industrial datasets from the International Software Benchmarking Standards Group (ISBSG) are used to train and validate the four models. The main ISBSG dataset was filtered and then divided into five datasets based on the productivity value of each project. Results show that the four models tend to overestimate in 80percent of the datasets, and the significance of the model inputs varies based on the selected model. Furthermore, the Cascade Correlation Neural Network outperforms the other three models in the majority of the datasets constructed on the Mean Absolute Residual criterion.

Connected through hyperedge HE-1 (induced by source paper "software development effort estimation using regression fuzzy models"):
  with members:
    - "analyzing the relationship between project productivity and environment factors in the use case points method"
    - "pareto efficient multi objective optimization for local tuning of analogy based estimation"
    - "a hybrid intelligent model for software cost estimation"

Connected through hyperedge HE-2 (induced by source paper "can we rely on smartphone applications"):
  with members:
    - "analogy based effort estimation a new method to discover set of analogies from dataset characteristics"

Connected through hyperedge HE-3 (induced by source paper "ensemble of learning project productivity in software effort based on use case points"):
  with members:
    - "analogy based effort estimation a new method to discover set of analogies from dataset characteristics"
    - "learning best k analogies from data distribution for case based software effort estimation"
    - "fuzzy model tree for early effort estimation"
    - "pareto efficient multi objective optimization for local tuning of analogy based estimation"
    - "a hybrid model for estimating software project effort from use case points"
    - "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation"

Connected through hyperedge HE-4 (induced by source paper "regularized fuzzy neural networks to aid effort forecasting in the construction and software development"):
  with members:
    - "influence of image classification accuracy on saliency map estimation"
    - "a hybrid model for estimating software project effort from use case points"

Connected through hyperedge HE-5 (induced by source paper "analyzing the relationship between project productivity and environment factors in the use case points method"):
  with members:
    - "model tree based adaption strategy for software effort estimation by analogy"
    - "analogy based effort estimation a new method to discover set of analogies from dataset characteristics"
    - "a hybrid model for estimating software project effort from use case points"
    - "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation"
    - "do feelings matter on the correlation of affects and the self assessed productivity in software engineering"

Connected through hyperedge HE-6 (induced by source paper "a hybrid model for estimating software project effort from use case points"):
  with members:
    - "analogy based effort estimation a new method to discover set of analogies from dataset characteristics"
    - "enhancing use case points estimation method using soft computing techniques"
    - "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation"

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)