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 "how often do single statement bugs occur the manysstubs4j dataset"):
  Abstract: Program repair is an important but difficult software engineering problem. One way to achieve a "sweet spot" of low false positive rates, while maintaining high enough recall to be usable, is to focus on repairing classes of simple bugs, such as bugs with single statement fixes, or that match a small set of bug templates (Long and Rinard, 2016; Pradel and Sen, 2018). However, it is very difficult to estimate the recall of repair techniques based on templates or based on repairing simple bugs, as there are no datasets about how often the associated bugs occur in code. To fill this gap, we provide two versions of the dataset containing 24412 and 153751 single statement bug-fix changes mined from 100 popular open-source Java Maven projects and from 1000 popular open-source Java projects respectively, annotated by whether they match any of a set of 16 bug templates, inspired by state-of-the-art program repair techniques. We also administer a repository of Maven dependencies for the 100 projects dataset to facilitate tools that require building the projects. We hope that this dataset will prove a resource both for future work in automatic program repair and also for future studies in empirical software engineering. In an initial analysis, we find that for both datasets about 33% of the simple bug fixes match the templates, indicating that a remarkable number of single-statement bugs can be repaired with a relatively small set of templates. Further, we find that SStuBs appear with a frequency of about one bug per 1600-2500 lines of code (as measured by the size of the project's latest version), allowing researchers to make an informed case about the potential impact of improved program repair methods.

Includes members:
  Member "sequencer sequence to sequence learning for end to end program repair":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "does unit tested code crash a case study of eclipse":
    also connected through other hyperedges:
      (none)
  Member "the codrep machine learning on source code competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequencer sequence to sequence learning for end to end program repair")
  Member "automatic software repair a bibliography":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a recurrent neural network based patch recommender for linux kernel bugs")
  Member "an empirical study on learning bug fixing patches in the wild via neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "equivalence of dataflow graphs via rewrite rules using a graph to sequence neural model")
  Member "on the naturalness of buggy code":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "big code big vocabulary open vocabulary models for source code")

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)