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 "on the feasibility of automated issue type prediction"):
  Abstract: Context: Issue tracking systems are used to track and describe tasks in the development process, e.g., requested feature improvements or reported bugs. However, past research has shown that the reported issue types often do not match the description of the issue. #R##N#Objective: We want to understand the overall maturity of the state of the art of issue type prediction with the goal to predict if issues are bugs and evaluate if we can improve existing models by incorporating manually specified knowledge about issues. #R##N#Method: We train different models for the title and description of the issue to account for the difference in structure between these fields, e.g., the length. Moreover, we manually detect issues whose description contains a null pointer exception, as these are strong indicators that issues are bugs. #R##N#Results: Our approach performs best overall, but not significantly different from an approach from the literature based on the fastText classifier from Facebook AI Research. The small improvements in prediction performance are due to structural information about the issues we used. We found that using information about the content of issues in form of null pointer exceptions is not useful. We demonstrate the usefulness of issue type prediction through the example of labelling bugfixing commits. #R##N#Conclusions: Issue type prediction works well and can be a useful tool if the use case allows either for a certain amount of missed bug reports or the prediction of too many issues as bug is acceptable.

Includes members:
  Member "the smartshark ecosystem for software repository mining":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "issues with szz an empirical assessment of the state of practice of defect prediction data collection")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bug or not bug report classification using n gram idf":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "identifying algorithm names in code comments")

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.SE(Software Engineering)