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: "fixminer mining relevant fix patterns for automated program repair"
  Abstract: Code comprehension is critical in software maintenance. Towards providing tools and approaches to support maintenance tasks, researchers have investigated various research lines related to how software code can be described in an abstract form. So far, studies on change pattern mining, code clone detection, or semantic patch inference have mainly adopted text-, token- and tree-based representations as the basis for computing similarity among code fragments. Although, in general, existing techniques form clusters of "similar" code, our experience in patch mining has revealed that clusters of patches formed by such techniques do not usually carry explainable semantics that can be associated to bug-fixing patterns. In this paper, we propose a novel, automated approach for mining semantically-relevant fix patterns based on an iterative, three-fold, clustering strategy. Our technique, FixMiner, leverages different tree representations for each round of clustering: the Abstract syntax tree, the edit actions tree, and the code context tree. We have evaluated FixMiner on thousands of software patches collected from open source projects. Preliminary results show that we are able to mine accurate patterns, efficiently exploiting change information in AST diff trees. Eventually, FixMiner yields patterns which can be associated to the semantics of the bugs that the associated patches address. We further leverage the mined patterns to implement an automated program repair pipeline with which we are able to correctly fix 25 bugs from the Defects4J benchmark. Beyond this quantitative performance, we show that the mined fix patterns are sufficiently relevant to produce patches with a high probability of correctness: 80% of FixMiner's generated plausible patches are correct, while the closest related works, namely HDRepair and SimFix, achieve respectively 26% and 70% of correctness.

Connected through hyperedge HE-1 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "tree2tree neural translation model for learning source code changes"
    - "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 "ifixr bug report driven program repair"):
  with members:
    - "d c a divide and conquer approach to ir based bug localization"
    - "tbar revisiting template based automated program repair"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "getafix learning to fix bugs automatically"
    - "avatar fixing semantic bugs with fix patterns of static analysis violations"
    - "on reliability of patch correctness assessment"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning quick fixes from code repositories"

Connected through hyperedge HE-4 (induced by source paper "analyzing the context of bug fixing changes in the openstack cloud computing platform"):
  with members:
    - "learning how to mutate source code from bug fixes"
    - "refdiff detecting refactorings in version histories"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "automatically extracting instances of code change patterns with ast analysis"

Connected through hyperedge HE-5 (induced by source paper "avatar fixing semantic bugs with fix patterns of static analysis violations"):
  with members:
    - "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"
    - "learning quick fixes from code repositories"
    - "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-6 (induced by source paper "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"):
  with members:
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "arja automated repair of java programs via multi objective genetic programming"
    - "an empirical analysis of the influence of fault space on search based automated program repair"
    - "identifying patch correctness in test based program repair"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "impact of tool support in patch construction"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "precise condition synthesis for program repair"

Connected through hyperedge HE-7 (induced by source paper "mining fix patterns for findbugs violations"):
  with members:
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "learning quick fixes from code repositories"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "watch out for this commit a study of influential software changes"):
  with members:
    - "tbar revisiting template based automated program repair"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "impact of tool support in patch construction"
    - "learning syntactic program transformations from examples"
    - "an analysis of the search spaces for generate and validate patch generation systems"
    - "harvesting fix hints in the history of bugs"
    - "a critical review of automatic patch generation learned from human written patches essay on the problem statement and the evaluation of automatic software repair"
    - "minthint automated synthesis of repair hints"

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)