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: "sampling as a baseline optimizer for search based software engineering"
  Abstract: Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method "SWAY", short for "the sampling way". Sway is very simple to implement and, in studies with various software engineering models, this sampling approach was found to be competitive with corresponding state-of-the-art evolutionary algorithms while requiring far less computation cost. Considering the simplicity and effectiveness of Sway, we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.

Connected through hyperedge HE-1 (induced by source paper "how to evaluate solutions in pareto based search based software engineering a critical review and methodological guidance"):
  with members:
    - "search based software engineering for self adaptive systems one survey five disappointments and six opportunities"
    - "understanding the automated parameter optimization on transfer learning for cpdp an empirical study"
    - "femosaa feature guided and knee driven multi objective optimization for self adaptive software"
    - "self adaptive trade off decision making for autoscaling cloud based services"
    - "how to read many objective solution sets in parallel coordinates"
    - "elegant object oriented software design via interactive evolutionary computation"

Connected through hyperedge HE-2 (induced by source paper "mat a simple yet strong baseline for identifying self admitted technical debt"):
  with members:
    - "towards good practices on building effective cnn baseline model for person re identification"
    - "simple baselines for human pose estimation and tracking"
    - "the effectiveness of instance normalization a strong baseline for single image dehazing"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "500 times faster than deep learning a case study exploring faster methods for text mining stackoverflow"
    - "show ask attend and answer a strong baseline for visual question answering"
    - "bellwethers a baseline method for transfer learning"
    - "easy over hard a case study on deep learning"

Connected through hyperedge HE-3 (induced by source paper "whence to learn transferring knowledge in configurable systems using beetle"):
  with members:
    - "finding faster configurations using flash"
    - "micky a cheaper alternative for selecting cloud instances"
    - "scout an experienced guide to find the best cloud configuration"
    - "transfer learning for improving model predictions in highly configurable software"
    - "bellwethers a baseline method for transfer learning"
    - "using bad learners to find good configurations"
    - "bestconfig tapping the performance potential of systems via automatic configuration tuning"
    - "faster discovery of faster system configurations with spectral learning"

Connected through hyperedge HE-4 (induced by source paper "faster creation of smaller test suites with snap"):
  with members:
    - "data driven search based software engineering"
    - "hyperparameter optimization for effort estimation"
    - "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"
    - "enhancing reuse of constraint solutions to improve symbolic execution"
    - "constrained sampling and counting universal hashing meets sat solving"

Connected through hyperedge HE-5 (induced by source paper "momit porting a javascript interpreter on a quarter coin"):
  with members:
    - "less is more exploiting the standard compiler optimization levels for better performance and energy consumption"

Connected through hyperedge HE-6 (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-7 (induced by source paper "data driven search based software engineering"):
  with members:
    - "finding faster configurations using flash"
    - "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"
    - "finding better active learners for faster literature reviews"
    - "transfer learning for improving model predictions in highly configurable software"
    - "short er reasoning about larger requirements models"
    - "using bad learners to find good configurations"

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)