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: "analogy based effort estimation a new method to discover set of analogies from dataset characteristics"
  Abstract: Analogy-based effort estimation (ABE) is one of the efficient methods for software effort estimation because of its outstanding performance and capability of handling noisy datasets. Conventional ABE models usually use the same number of analogies for all projects in the datasets in order to make good estimates. The authors' claim is that using same number of analogies may produce overall best performance for the whole dataset but not necessarily best performance for each individual project. Therefore there is a need to better understand the dataset characteristics in order to discover the optimum set of analogies for each project rather than using a static k nearest projects. Method: We propose a new technique based on Bisecting k-medoids clustering algorithm to come up with the best set of analogies for each individual project before making the prediction. Results & Conclusions: With Bisecting k-medoids it is possible to better understand the dataset characteristic, and automatically find best set of analogies for each test project. Performance figures of the proposed estimation method are promising and better than those of other regular ABE models

Connected through hyperedge HE-1 (induced by source paper "can we rely on smartphone applications"):
  with members:
    - "neural network models for software development effort estimation a comparative study"

Connected through hyperedge HE-2 (induced by source paper "ensemble of learning project productivity in software effort based on use case points"):
  with members:
    - "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"
    - "neural network models for software development effort estimation a comparative study"
    - "an empirical evaluation of ensemble adjustment methods for analogy based effort estimation"

Connected through hyperedge HE-3 (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"
    - "a hybrid model for estimating software project effort from use case points"
    - "neural network models for software development effort estimation a comparative study"
    - "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-4 (induced by source paper "a hybrid model for estimating software project effort from use case points"):
  with members:
    - "enhancing use case points estimation method using soft computing techniques"
    - "neural network models for software development effort estimation a comparative study"
    - "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)