human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a hybrid model for estimating software project effort from use case points"):
  Abstract: Display Omitted Project productivity is key factor in estimating effort from UCP.Project productivity must be flexible and adjustable when historical data is available.Environmental factors are good indicators for predicting productivity.Class decomposition is a good method to produce fine-grained productivity labels.Using fixed productivity ratios is not a good practice from managerial perspective. Early software effort estimation is a hallmark of successful software project management. Building a reliable effort estimation model usually requires historical data. Unfortunately, since the information available at early stages of software development is scarce, it is recommended to use software size metrics as key cost factor of effort estimation. Use Case Points (UCP) is a prominent size measure designed mainly for object-oriented projects. Nevertheless, there are no established models that can translate UCP into its corresponding effort; therefore, most models use productivity as a second cost driver. The productivity in those models is usually guessed by experts and does not depend on historical data, which makes it subject to uncertainty. Thus, these models were not well examined using a large number of historical data. In this paper, we designed a hybrid model that consists of classification and prediction stages using a support vector machine and radial basis neural networks. The proposed model was constructed over a large number of observations collected from industrial and student projects. The proposed model was compared against previous UCP prediction models. The validation and empirical results demonstrated that the proposed model significantly surpasses these models on all datasets. The main conclusion is that the environmental factors of UCP can be used to classify and estimate productivity.

Includes members:
  Member "analogy based effort estimation a new method to discover set of analogies from dataset characteristics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "can we rely on smartphone applications")
  Member "enhancing use case points estimation method using soft computing techniques":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking machine learning technologies for software defect detection")
  Member "neural network models for software development effort estimation a comparative study":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "software development effort estimation using regression fuzzy models")
  Member "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ensemble of learning project productivity in software effort based on use case points")

Question: Please tell me which class the source paper 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)