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: "can you explain that better comprehensible text analytics for se applications"
  Abstract: Text mining methods are used for a wide range of Software Engineering (SE) tasks. The biggest challenge of text mining is high dimensional data, i.e., a corpus of documents can contain $10^4$ to $10^6$ unique words. To address this complexity, some very convoluted text mining methods have been applied. Is that complexity necessary? Are there simpler ways to quickly generate models that perform as well as the more convoluted methods and also be human-readable? #R##N#To answer these questions, we explore a combination of LDA (Latent Dirichlet Allocation) and FFTs (Fast and Frugal Trees) to classify NASA software bug reports from six different projects. Designed using principles from psychological science, FFTs return very small models that are human-comprehensible. When compared to the commonly used text mining method and a recent state-of-the-art-system (search-based SE method that automatically tune the control parameters of LDA), these FFT models are very small (a binary tree of depth $d = 4$ that references only 4 topics) and hence easy to understand. They were also faster to generate and produced similar or better severity predictions. #R##N#Hence we can conclude that, at least for datasets explored here, convoluted text mining models can be deprecated in favor of simpler method such as LDA+FFTs. At the very least, we recommend LDA+FFTs (a) when humans need to read, understand, and audit a model or (b) as an initial baseline method for the SE researchers exploring text artifacts from software projects.

Connected through hyperedge HE-1 (induced by source paper "is one hyperparameter optimizer enough"):
  with members:
    - "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"
    - "building better quality predictors using epsilon dominance"
    - "bellwethers a baseline method for transfer learning"
    - "better data is better than better data miners benefits of tuning smote for defect prediction"
    - "riot a novel stochastic method for rapidly configuring cloud based workflows"
    - "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)