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: "negative results for software effort estimation"
  Abstract: Context:More than half the literature on software effort estimation (SEE) focuses on comparisons of new estimation methods. Surprisingly, there are no studies comparing state of the art latest methods with decades-old approaches. Objective:To check if new SEE methods generated better estimates than older methods. Method: Firstly, collect effort estimation methods ranging from "classical" COCOMO (parametric estimation over a pre-determined set of attributes) to "modern" (reasoning via analogy using spectral-based clustering plus instance and feature selection, and a recent "baseline method" proposed in ACM Transactions on Software Engineering).Secondly, catalog the list of objections that lead to the development of post-COCOMO estimation methods.Thirdly, characterize each of those objections as a comparison between newer and older estimation methods.Fourthly, using four COCOMO-style data sets (from 1991, 2000, 2005, 2010) and run those comparisons experiments.Fifthly, compare the performance of the different estimators using a Scott-Knott procedure using (i) the A12 effect size to rule out "small" differences and (ii) a 99% confident bootstrap procedure to check for statistically different groupings of treatments). Results: The major negative results of this paper are that for the COCOMO data sets, nothing we studied did any better than Boehm's original procedure. Conclusions: When COCOMO-style attributes are available, we strongly recommend (i) using that data and (ii) use COCOMO to generate predictions. We say this since the experiments of this paper show that, at least for effort estimation,how data is collected is more important than what learner is applied to that data.

Connected through hyperedge HE-1 (induced by source paper "sequential model optimization for software process control"):
  with members:
    - "finding faster configurations using flash"
    - "data driven search based software engineering"
    - "warmstarting of model based algorithm configuration"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"

Connected through hyperedge HE-2 (induced by source paper "hyperparameter optimization for effort estimation"):
  with members:
    - "the impact of automated parameter optimization on defect prediction models"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "data driven search based software engineering"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "tuning for software analytics"
    - "locally weighted naive bayes"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-3 (induced by source paper "why software effort estimation needs sbse"):
  with members:
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "data driven search based software engineering"
    - "a deep learning model for estimating story points"
    - "tuning for software analytics"
    - "locally weighted naive bayes"

Connected through hyperedge HE-4 (induced by source paper "bellwethers a baseline method for transfer learning"):
  with members:
    - "less is more minimizing code reorganization using xtree"
    - "better predictors for issue lifetime"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "tuning for software analytics"
    - "smote synthetic minority over sampling technique"

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)