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 "sorting and transforming program repair ingredients via deep learning code similarities"):
  Abstract: In the field of automated program repair, the redundancy assumption claims large programs contain the seeds of their own repair. However, most redundancy-based program repair techniques do not reason about the repair ingredients– the code that is reused to craft a patch. We aim to reason about the repair ingredients by using code similarities to prioritize and transform statements in a codebase for patch generation. Our approach, DeepRepair, relies on deep learning to reason about code similarities. Code fragments at well-defined levels of granularity in a codebase can be sorted according to their similarity to suspicious elements (i.e., code elements that contain suspicious statements) and statements can be transformed by mapping out-of-scope identifiers to similar identifiers in scope. We examined these new search strategies for patch generation with respect to effectiveness from the viewpoint of a software maintainer. Our comparative experiments were executed on six open-source Java projects including 374 buggy program revisions and consisted of 19,949 trials spanning 2,616 days of computation time. Deep-Repair’s search strategy using code similarities generally found compilable ingredients faster than the baseline, jGenProg, but this improvement neither yielded test-adequate patches in fewer attempts (on average) nor found significantly more patches (on average) than the baseline. Although the patch counts were not statistically different, there were notable differences between the nature of DeepRepair patches and jGenProg patches. The results show that our learning-based approach finds patches that cannot be found by existing redundancy-based repair techniques.

Includes members:
  Member "on learning meaningful code changes via neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "automatic software repair a bibliography":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a recurrent neural network based patch recommender for linux kernel bugs")
  Member "automatic repair of real bugs in java a large scale experiment on the defects4j dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "nopol automatic repair of conditional statement bugs in java programs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "automated correction for syntax errors in programming assignments using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "big code big vocabulary open vocabulary models for source code")
  Member "sk_p a neural program corrector for moocs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a study of pyramid structure for code correction")
  Member "mining software repair models for reasoning on the search space of automated program fixing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "learning api usages from bytecode a statistical approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a comprehensive study of bloated dependencies in the maven ecosystem")

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)