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: "why is differential evolution better than grid search for tuning defect predictors"
  Abstract: Context: One of the black arts of data mining is learning the magic parameters which control the learners. In software analytics, at least for defect prediction, several methods, like grid search and differential evolution (DE), have been proposed to learn these parameters, which has been proved to be able to improve the performance scores of learners. #R##N#Objective: We want to evaluate which method can find better parameters in terms of performance score and runtime cost. #R##N#Methods: This paper compares grid search to differential evolution, which is an evolutionary algorithm that makes extensive use of stochastic jumps around the search space. #R##N#Results: We find that the seemingly complete approach of grid search does no better, and sometimes worse, than the stochastic search. When repeated 20 times to check for conclusion validity, DE was over 210 times faster than grid search to tune Random Forests on 17 testing data sets with F-Measure #R##N#Conclusions: These results are puzzling: why does a quick partial search be just as effective as a much slower, and much more, extensive search? To answer that question, we turned to the theoretical optimization literature. Bergstra and Bengio conjecture that grid search is not more effective than more randomized searchers if the underlying search space is inherently low dimensional. This is significant since recent results show that defect prediction exhibits very low intrinsic dimensionality-- an observation that explains why a fast method like DE may work as well as a seemingly more thorough grid search. This suggests, as a future research direction, that it might be possible to peek at data sets before doing any optimization in order to match the optimization algorithm to the problem at hand.

Connected through hyperedge HE-1 (induced by source paper "how to dodge complex software analytics"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "the impact of automated parameter optimization on defect prediction models"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "is one hyperparameter optimizer enough"

Connected through hyperedge HE-2 (induced by source paper "is ai different for se"):
  with members:
    - "how to dodge complex software analytics"
    - "a benchmark study on sentiment analysis for software engineering research"
    - "applications of psychological science for actionable analytics"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "better software analytics via duo data mining algorithms using used by optimizers"
    - "bellwethers a baseline method for transfer learning"
    - "less is more minimizing code reorganization using xtree"

Connected through hyperedge HE-3 (induced by source paper "enhanced labeling of issue reports with f 3 t"):
  with members:
    - "how to dodge complex software analytics"
    - "replication can improve prior results a github study of pull request acceptance"
    - "applications of psychological science for actionable analytics"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "finding better active learners for faster literature reviews"
    - "we don t need another hero the impact of heroes on software development"
    - "bellwethers a baseline method for transfer learning"
    - "easy over hard a case study on deep learning"

Connected through hyperedge HE-4 (induced by source paper "better security bug report classification via hyperparameter optimization"):
  with members:
    - "bad smells in software analytics papers"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "less is more minimizing code reorganization using xtree"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "a brief survey of text mining classification clustering and extraction techniques"
    - "tuning for software analytics"

Connected through hyperedge HE-5 (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"
    - "negative results for software effort estimation"
    - "tuning for software analytics"

Connected through hyperedge HE-6 (induced by source paper "predicting breakdowns in cloud services with spike"):
  with members:
    - "hyperparameter optimization for effort estimation"
    - "less is more minimizing code reorganization using xtree"
    - "easy over hard a case study on deep learning"
    - "tuning for software analytics"
    - "a critical review of recurrent neural networks for sequence learning"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-7 (induced by source paper "better software analytics via duo data mining algorithms using used by optimizers"):
  with members:
    - "finding faster configurations using flash"
    - "applications of psychological science for actionable analytics"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "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"
    - "is one hyperparameter optimizer enough"
    - "an improved generic bet and run strategy for speeding up stochastic local search"

Connected through hyperedge HE-8 (induced by source paper "bad smells in software analytics papers"):
  with members:
    - "the impact of automated parameter optimization on defect prediction models"
    - "is better data better than better data miners on the benefits of tuning smote for defect prediction"
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "data driven search based software engineering"
    - "we don t need another hero the impact of heroes on software development"
    - "revisiting unsupervised learning for defect prediction"
    - "what is the connection between issues bugs and enhancements lessons learned from 800 software projects"
    - "easy over hard a case study on deep learning"

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)