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: "impact of tool support in patch construction"
  Abstract: In this work, we investigate the practice of patch construction in the Linux kernel development, focusing on the differences between three patching processes: (1) patches crafted entirely manually to fix bugs, (2) those that are derived from warnings of bug detection tools, and (3) those that are automatically generated based on fix patterns. With this study, we provide to the research community concrete insights on the practice of patching as well as how the development community is currently embracing research and commercial patching tools to improve productivity in repair. The result of our study shows that tool-supported patches are increasingly adopted by the developer community while manually-written patches are accepted more quickly. Patch application tools enable developers to remain committed to contributing patches to the code base. Our findings also include that, in actual development processes, patches generally implement several change operations spread over the code, even for patches fixing warnings by bug detection tools. Finally, this study has shown that there is an opportunity to directly leverage the output of bug detection tools to readily generate patches that are appropriate for fixing the problem, and that are consistent with manually-written patches.

Connected through hyperedge HE-1 (induced by source paper "learning to catch security patches"):
  with members:
    - "a manually curated dataset of fixes to vulnerabilities of open source software"
    - "patchnet a tool for deep patch classification"
    - "the coming era of alphahacking a survey of automatic software vulnerability detection exploitation and patching techniques"
    - "a practical approach to the automatic classification of security relevant commits"
    - "vuldeepecker a deep learning based system for vulnerability detection"
    - "a survey of machine learning for big code and naturalness"
    - "survey of automated vulnerability detection and exploit generation techniques in cyber reasoning systems"
    - "why should i trust you explaining the predictions of any classifier"

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"
    - "fixminer mining relevant fix patterns for automated program repair"

Connected through hyperedge HE-3 (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-4 (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"
    - "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"
    - "mining fix patterns for findbugs violations"
    - "precise condition synthesis for program repair"

Connected through hyperedge HE-5 (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:
    - "fixminer mining relevant fix patterns for automated program repair"
    - "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"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "precise condition synthesis for program repair"

Connected through hyperedge HE-6 (induced by source paper "watch out for this commit a study of influential software changes"):
  with members:
    - "tbar revisiting template based automated program repair"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "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)