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 "avatar fixing semantic bugs with fix patterns of static analysis violations"):
  Abstract: Fix pattern-based patch generation is a promising direction in Automated Program Repair (APR). Notably, it has been demonstrated to produce more acceptable and correct patches than the patches obtained with mutation operators through genetic programming. The performance of pattern-based APR systems, however, depends on the fix ingredients mined from fix changes in development histories. Unfortunately, collecting a reliable set of bug fixes in repositories can be challenging. In this paper, we propose to investigate the possibility in an APR scenario of leveraging code changes that address violations by static bug detection tools. To that end, we build the AVATAR APR system, which exploits fix patterns of static analysis violations as ingredients for patch generation. Evaluated on the Defects4J benchmark, we show that, assuming a perfect localization of faults, AVATAR can generate correct patches to fix 34/39 bugs. We further find that AVATAR yields performance metrics that are comparable to that of the closely-related approaches in the literature. While AVATAR outperforms many of the state-of-the-art pattern-based APR systems, it is mostly complementary to current approaches. Overall, our study highlights the relevance of static bug finding tools as indirect contributors of fix ingredients for addressing code defects identified with functional test cases.

Includes members:
  Member "fixminer mining relevant fix patterns for automated program repair":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair")
  Member "automatic repair of real bugs in java a large scale experiment on the defects4j dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explaining deep neural networks using spectrum based fault localization")
  Member "learning quick fixes from code repositories":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tbar revisiting template based automated program repair")
  Member "dissection of a bug dataset anatomy of 395 patches from defects4j":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair")
  Member "nopol automatic repair of conditional statement bugs in java programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "impact of tool support in patch construction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to catch security patches")
  Member "mining fix patterns for findbugs violations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ifixr bug report driven 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)