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: "d c a divide and conquer approach to ir based bug localization"
  Abstract: Many automated tasks in software maintenance rely on information retrieval techniques to identify specific information within unstructured data. Bug localization is such a typical task, where text in a bug report is analyzed to identify file locations in the source code that can be associated to the reported bug. Despite the promising results, the performance offered by IR-based bug localization tools is still not significant for large adoption. We argue that one reason could be the attempt to build a one-size-fits-all approach. In this paper, we extensively study the performance of state-of-the-art bug localization tools, focusing on query formulation and its importance with respect to the localization performance. Building on insights from this study, we propose a new learning approach where multiple classifier models are trained on clear-cut sets of bug-location pairs. Concretely, we apply a gradient boosting supervised learning approach to various sets of bug reports whose localizations appear to be successful with specific types of features. The training scenario builds on our findings that the various state-of-the-art localization tools can be highly performant for specific sets of bug reports. We implement D&C, which computes appropriate weights that should be assigned to the similarity measurements between pairs of information token types. Experimental results on large and up-to-date datasets reveal that D&C outperforms state-of-the-art tools. On average, the experiments yield an MAP score of 0.52, and an MRR score of 0.63 with a curated dataset, which provides a substantial performance improvement over all tools: MAP is improved by between 4 and up to 10 percentage points, while MRR is improved by between 1 and up to 12. Finally, we note that D&C is stable in its localization performance: around 50% of bugs can be located at Top1, 77% at Top5 and 85% at Top10.

Connected through hyperedge HE-1 (induced by source paper "ifixr bug report driven program repair"):
  with members:
    - "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"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"

Connected through hyperedge HE-2 (induced by source paper "fixminer mining relevant fix patterns for automated program repair"):
  with members:
    - "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"
    - "learning quick fixes from code repositories"

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)