human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "learning quick fixes from code repositories"
  Abstract: Code analyzers such as Error Prone and FindBugs detect code patterns symptomatic of bugs, performance issues, or bad style. These tools express patterns as quick fixes that detect and rewrite unwanted code. However, it is difficult to come up with new quick fixes and decide which ones are useful and frequently appear in real code. We propose to rely on the collective wisdom of programmers and learn quick fixes from revision histories in software repositories. We present REVISAR, a tool for discovering common Java edit patterns in code repositories. Given code repositories and their revision histories, REVISAR (i) identifies code edits from revisions and (ii) clusters edits into sets that can be described using an edit pattern. The designers of code analyzers can then inspect the patterns and add the corresponding quick fixes to their tools. We ran REVISAR on nine popular GitHub projects, and it discovered 89 useful edit patterns that appeared in 3 or more projects. Moreover, 64% of the discovered patterns did not appear in existing tools. We then conducted a survey with 164 programmers from 124 projects and found that programmers significantly preferred eight out of the nine of the discovered patterns. Finally, we submitted 16 pull requests applying our patterns to 9 projects and, at the time of the writing, programmers accepted 6 (60%) of them. The results of this work aid toolsmiths in discovering quick fixes and making informed decisions about which quick fixes to prioritize based on patterns programmers actually apply in practice.

Connected through hyperedge HE-1 (induced by source paper "tbar revisiting template based automated program repair"):
  with members:
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "avatar fixing semantic bugs with fix patterns of static analysis violations"
    - "on reliability of patch correctness assessment"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "automatic software repair a bibliography"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"

Connected through hyperedge HE-2 (induced by source paper "getafix learning to fix bugs automatically"):
  with members:
    - "learning to represent edits"
    - "deepbugs a learning approach to name based bug detection"
    - "coming a tool for mining change pattern instances from git commits"
    - "semantic code repair using neuro symbolic transformation networks"
    - "a survey of machine learning for big code and naturalness"
    - "learning syntactic program transformations from examples"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "npefix automatic runtime repair of null pointer exceptions in java"

Connected through hyperedge HE-3 (induced by source paper "learning and suggesting source code changes from version history a systematic review"):
  with members:
    - "connecting software metrics across versions to predict defects"
    - "refdiff detecting refactorings in version histories"
    - "learning syntactic program transformations from examples"

Connected through hyperedge HE-4 (induced by source paper "fixminer mining relevant fix patterns for automated program repair"):
  with members:
    - "d c a divide and conquer approach to ir based bug localization"
    - "tbar revisiting template based automated program repair"
    - "ifixr bug report driven program repair"
    - "avatar fixing semantic bugs with fix patterns of static analysis violations"
    - "automatic software repair a bibliography"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"

Connected through hyperedge HE-5 (induced by source paper "learning to represent edits"):
  with members:
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "response generation by context aware prototype editing"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep differential recurrent neural networks"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "tree to tree neural networks for program translation"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-6 (induced by source paper "avatar fixing semantic bugs with fix patterns of static analysis violations"):
  with members:
    - "fixminer mining relevant fix patterns for automated program repair"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"
    - "dissection of a bug dataset anatomy of 395 patches from defects4j"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "impact of tool support in patch construction"
    - "mining fix patterns for findbugs violations"
    - "precise condition synthesis for program repair"

Connected through hyperedge HE-7 (induced by source paper "mining fix patterns for findbugs violations"):
  with members:
    - "fixminer mining relevant fix patterns for automated program repair"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "automatically generating commit messages from diffs using neural machine translation"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "a survey of machine learning for big code and naturalness"
    - "cross language learning for program classification using bilateral tree based convolutional neural networks"
    - "learning syntactic program transformations from examples"
    - "precise condition synthesis for program repair"

Question: Please tell me which class the center node 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)