human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting good configurations for github and stack overflow topic models"):
  Abstract: Software repositories contain large amounts of textual data, ranging from source code comments and issue descriptions to questions, answers, and comments on Stack Overflow. To make sense of this textual data, topic modelling is frequently used as a text-mining tool for the discovery of hidden semantic structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used topic model that aims to explain the structure of a corpus by grouping texts. LDA requires multiple parameters to work well, and there are only rough and sometimes conflicting guidelines available on how these parameters should be set. In this paper, we contribute (i) a broad study of parameters to arrive at good local optima for GitHub and Stack Overflow text corpora, (ii) an a-posteriori characterisation of text corpora related to eight programming languages, and (iii) an analysis of corpus feature importance via per-corpus LDA configuration. We find that (1) popular rules of thumb for topic modelling parameter configuration are not applicable to the corpora used in our experiments, (2) corpora sampled from GitHub and Stack Overflow have different characteristics and require different configurations to achieve good model fit, and (3) we can predict good configurations for unseen corpora reliably. These findings support researchers and practitioners in efficiently determining suitable configurations for topic modelling when analysing textual data contained in software repositories.

Includes members:
  Member "categorizing the content of github readme files":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conflict and cooperation ai research and development in terms of the economy of conventions")
  Member "what is wrong with topic modeling and how to fix it using search based software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "better software analytics via duo data mining algorithms using used by optimizers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to dodge complex software analytics")
  Member "automated algorithm selection survey and perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online preselection with context information under the plackett luce model")
  Member "data driven search based software engineering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster creation of smaller test suites with snap")
  Member "sotorrent reconstructing and analyzing the evolution of stack overflow posts":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross dataset design discussion mining")
  Member "a case study of algorithm selection for the traveling thief problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the node weight dependent traveling salesperson problem approximation algorithms and randomized search heuristics")
  Member "aslib a benchmark library for algorithm selection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "extreme algorithm selection with dyadic feature representation")

Question: Please tell me which class the source paper 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.CL(Computation and Language)