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 evaluation of ensemble adjustment methods for analogy based effort estimation"
  Abstract: Ensembles of adjustment methods are not always superior to single methods.Ensembles of linear methods are more accurate than ensembles of nonlinear methods.Adjustment methods based on GA and NN got the worst accuracy.Changing the value of k makes the prediction models behave diversely.RTM variants is the top ranked type based on Scott-Knott and two-way ANOVA. ContextEffort adjustment is an essential part of analogy-based effort estimation, used to tune and adapt nearest analogies in order to produce more accurate estimations. Currently, there are plenty of adjustment methods proposed in literature, but there is no consensus on which method produces more accurate estimates and under which settings. ObjectiveThis paper investigates the potential of ensemble learning for variants of adjustment methods used in analogy-based effort estimation. The number k of analogies to be used is also investigated. MethodWe perform a large scale comparison study where many ensembles constructed from n out of 40 possible valid variants of adjustment methods are applied to eight datasets. The performance of each method was evaluated based on standardized accuracy and effect size. ResultsThe results have been subjected to statistical significance testing, and show reasonable significant improvements on the predictive performance where ensemble methods are applied. ConclusionOur conclusions suggest that ensembles of adjustment methods can work well and achieve good performance, even though they are not always superior to single methods. We also recommend constructing ensembles from only linear adjustment methods, as they have shown better performance and were frequently ranked higher.

Connected through hyperedge HE-1 (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"
    - "neural network models for software development effort estimation a comparative study"

Connected through hyperedge HE-2 (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"
    - "neural network models for software development effort estimation a comparative study"
    - "do feelings matter on the correlation of affects and the self assessed productivity in software engineering"

Connected through hyperedge HE-3 (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"
    - "neural network models for software development effort estimation a comparative study"

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)