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 "ifixr bug report driven program repair"):
  Abstract: Issue tracking systems are commonly used in modern software development for collecting feedback from users and developers. An ultimate automation target of software maintenance is then the systematization of patch generation for user-reported bugs. Although this ambition is aligned with the momentum of automated program repair, the literature has, so far, mostly focused on generate-and- validate setups where fault localization and patch generation are driven by a well-defined test suite. On the one hand, however, the common (yet strong) assumption on the existence of relevant test cases does not hold in practice for most development settings: many bugs are reported without the available test suite being able to reveal them. On the other hand, for many projects, the number of bug reports generally outstrips the resources available to triage them. Towards increasing the adoption of patch generation tools by practitioners, we investigate a new repair pipeline, iFixR, driven by bug reports: (1) bug reports are fed to an IR-based fault localizer; (2) patches are generated from fix patterns and validated via regression testing; (3) a prioritized list of generated patches is proposed to developers. We evaluate iFixR on the Defects4J dataset, which we enriched (i.e., faults are linked to bug reports) and carefully-reorganized (i.e., the timeline of test-cases is naturally split). iFixR generates genuine/plausible patches for 21/44 Defects4J faults with its IR-based fault localizer. iFixR accurately places a genuine/plausible patch among its top-5 recommendation for 8/13 of these faults (without using future test cases in generation-and-validation).

Includes members:
  Member "d c a divide and conquer approach to ir based bug localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fixminer mining relevant fix patterns for automated program repair")
  Member "tbar revisiting template based automated program repair":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fixminer mining relevant fix patterns for automated program repair")
  Member "sorting and transforming program repair ingredients via deep learning code similarities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "checking smart contracts with structural code embedding")
  Member "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "getafix learning to fix bugs automatically":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial examples for models of code")
  Member "avatar fixing semantic bugs with fix patterns of static analysis violations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tbar revisiting template based automated program repair")
  Member "on reliability of patch correctness assessment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "tbar revisiting template based automated program repair")
  Member "fixminer mining relevant fix patterns for automated program repair":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair")

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)