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: "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
  Abstract: Defects4J is a large, peer-reviewed, structured dataset of real-world Java bugs. Each bug in Defects4J comes with a test suite and at least one failing test case that triggers the bug. In this paper, we report on an experiment to explore the effectiveness of automatic test-suite based repair on Defects4J. The result of our experiment shows that the considered state-of-the-art repair methods can generate patches for 47 out of 224 bugs. However, those patches are only test-suite adequate, which means that they pass the test suite and may potentially be incorrect beyond the test-suite satisfaction correctness criterion. We have manually analyzed 84 different patches to assess their real correctness. In total, 9 real Java bugs can be correctly repaired with test-suite based repair. This analysis shows that test-suite based repair suffers from under-specified bugs, for which trivial or incorrect patches still pass the test suite. With respect to practical applicability, it takes on average 14.8 minutes to find a patch. The experiment was done on a scientific grid, totaling 17.6 days of computation time. All the repair systems and experimental results are publicly available on Github in order to facilitate future research on automatic repair.

Connected through hyperedge HE-1 (induced by source paper "e apr mapping the effectiveness of automated program repair"):
  with members:
    - "learning the relation between code features and code transforms with structured prediction"
    - "how different is it between machine generated and developer provided patches an empirical study on the correct patches generated by automated program repair techniques"
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "empirical review of java program repair tools a large scale experiment on 2 141 bugs and 23 551 repair attempts"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "arja automated repair of java programs via multi objective genetic programming"
    - "identifying patch correctness in test based program repair"

Connected through hyperedge HE-2 (induced by source paper "automated repair of resource leaks in android applications"):
  with members:
    - "automatic software repair a bibliography"
    - "anadroid malware analysis of android with user supplied predicates"

Connected through hyperedge HE-3 (induced by source paper "sequencer sequence to sequence learning for end to end program repair"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "maybe deep neural networks are the best choice for modeling source code"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "an empirical study of fault localization families and their combinations"
    - "automatic software repair a bibliography"
    - "code vectors understanding programs through embedded abstracted symbolic traces"
    - "learning to generate corrective patches using neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "automated patch assessment for program repair at scale"):
  with members:
    - "how different is it between machine generated and developer provided patches an empirical study on the correct patches generated by automated program repair techniques"
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "validation of automatically generated patches an appetizer"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "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"
    - "empirical review of java program repair tools a large scale experiment on 2 141 bugs and 23 551 repair attempts"

Connected through hyperedge HE-5 (induced by source paper "sorting and transforming program repair ingredients via deep learning code similarities"):
  with members:
    - "on learning meaningful code changes via neural machine translation"
    - "automatic software repair a bibliography"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "sk_p a neural program corrector for moocs"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "learning api usages from bytecode a statistical approach"
    - "on the naturalness of buggy code"

Connected through hyperedge HE-6 (induced by source paper "empirical review of java program repair tools a large scale experiment on 2 141 bugs and 23 551 repair attempts"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "bears an extensible java bug benchmark for automatic program repair studies"
    - "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"
    - "automatic software repair a bibliography"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "dissection of a bug dataset anatomy of 395 patches from defects4j"
    - "arja automated repair of java programs via multi objective genetic programming"

Connected through hyperedge HE-7 (induced by source paper "astor exploring the design space of generate and validate program repair beyond genprog"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "ultra large repair search space with automatically mined templates the cardumen mode of astor"
    - "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"
    - "dynamic patch generation for null pointer exceptions using metaprogramming"

Connected through hyperedge HE-8 (induced by source paper "how different is it between machine generated and developer provided patches an empirical study on the correct patches generated by automated program repair techniques"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "an empirical study of fault localization families and their combinations"
    - "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"
    - "attention please consider mockito when evaluating newly proposed automated program repair techniques"
    - "an empirical analysis of the influence of fault space on search based automated program repair"
    - "an analysis of the search spaces for generate and validate patch generation systems"

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)