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: "hyperparameter optimization for effort estimation"
  Abstract: Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g. defect prediction and text mining) but, so far, has not been extensively explored for effort estimation. Accordingly, this paper seeks simple, automatic, effective and fast methods for finding good tunings for automatic software effort estimation. #R##N#We introduce a hyperparameter optimization architecture called OIL (Optimized Inductive Learning). We test OIL on a wide range of hyperparameter optimizers using data from 945 software projects. After tuning, large improvements in effort estimation accuracy were observed (measured in terms of standardized accuracy). #R##N#From those results, we recommend using regression trees (CART) tuned by different evolution combine with default analogy-based estimator. This particular combination of learner and optimizers often achieves in a few hours what other optimizers need days to weeks of CPU time to accomplish. #R##N#An important part of this analysis is its reproducibility and refutability. All our scripts and data are on-line. It is hoped that this paper will prompt and enable much more research on better methods to tune software effort estimators.

Connected through hyperedge HE-1 (induced by source paper "cross dataset design discussion mining"):
  with members:
    - "on the feasibility of transfer learning code smells using deep learning"
    - "methodology matters how we study socio technical aspects in software engineering"
    - "sotorrent reconstructing and analyzing the evolution of stack overflow posts"
    - "replication studies considered harmful"
    - "universal language model fine tuning for text classification"
    - "evolution of statistical analysis in empirical software engineering research current state and steps forward"
    - "advances in pre training distributed word representations"
    - "rationale in development chat messages an exploratory study"

Connected through hyperedge HE-2 (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-3 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "data driven search based software engineering"
    - "riot a stochastic based method for workflow scheduling in the cloud"
    - "revisiting unsupervised learning for defect prediction"
    - "beyond evolutionary algorithms for search based software engineering"
    - "a survey of symbolic execution techniques"
    - "sampling as a baseline optimizer for search based software engineering"
    - "enhancing reuse of constraint solutions to improve symbolic execution"
    - "constrained sampling and counting universal hashing meets sat solving"

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"
    - "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"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"

Connected through hyperedge HE-5 (induced by source paper "software engineering for fairness a case study with hyperparameter optimization"):
  with members:
    - "finding faster configurations using flash"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "mitigating unwanted biases with adversarial learning"
    - "automated directed fairness testing"
    - "a convex framework for fair regression"
    - "fairness testing testing software for discrimination"
    - "equality of opportunity in supervised learning"
    - "fairtest discovering unwarranted associations in data driven applications"

Connected through hyperedge HE-6 (induced by source paper "predicting breakdowns in cloud services with spike"):
  with members:
    - "less is more minimizing code reorganization using xtree"
    - "easy over hard a case study on deep learning"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "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"

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)