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: "beyond evolutionary algorithms for search based software engineering"
  Abstract: Abstract   Context  Evolutionary algorithms typically require large number of evaluations (of solutions) to converge – which can be very slow and expensive to evaluate.    Objective  To solve search-based software engineering (SE) problems, using fewer evaluations than evolutionary methods.    Method  Instead of mutating a small population, we build a very large initial population which is then culled using a recursive bi-clustering chop approach. We evaluate this approach on multiple SE models, unconstrained as well as constrained, and compare its performance with standard evolutionary algorithms.    Results  Using just a few evaluations (under 100), we can obtain comparable results to state-of-the-art evolutionary algorithms.    Conclusion  Just because something works, and is widespread use, does not necessarily mean that there is no value in seeking methods to improve that method. Before undertaking search-based SE optimization tasks using traditional EAs, it is recommended to try other techniques, like those explored here, to obtain the same results with fewer evaluations.

Connected through hyperedge HE-1 (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"
    - "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-2 (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-3 (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-4 (induced by source paper "finding faster configurations using flash"):
  with members:
    - "easy over hard a case study on deep learning"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "riot a novel stochastic method for rapidly configuring cloud based workflows"):
  with members:
    - "transfer learning for improving model predictions in highly configurable software"
    - "using bad learners to find good configurations"
    - "faster discovery of faster system configurations with spectral learning"
    - "sampling as a baseline optimizer for search based software engineering"
    - "deep api learning"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"

Connected through hyperedge HE-6 (induced by source paper "flash a faster optimizer for sbse tasks"):
  with members:
    - "using bad learners to find good configurations"
    - "an uncertainty aware approach to optimal configuration of stream processing systems"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"

Connected through hyperedge HE-7 (induced by source paper "sampling as a baseline optimizer for search based software engineering"):
  with members:
    - "riot a novel stochastic method for rapidly configuring cloud based workflows"
    - "easy over hard a case study on deep learning"
    - "a note on the triangle inequality for the jaccard distance"
    - "tuning for software analytics"

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)