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: "an empirical analysis of the influence of fault space on search based automated program repair"
  Abstract: Automated program repair (APR) has attracted great research attention, and various techniques have been proposed. Search-based APR is one of the most important categories among these techniques. Existing researches focus on the design of effective mutation operators and searching algorithms to better find the correct patch. Despite various efforts, the effectiveness of these techniques are still limited by the search space explosion problem. One of the key factors attribute to this problem is the quality of fault spaces as reported by existing studies. This motivates us to study the importance of the fault space to the success of finding a correct patch. Our empirical study aims to answer three questions. Does the fault space significantly correlate with the performance of search-based APR? If so, are there any indicative measurements to approximate the accuracy of the fault space before applying expensive APR techniques? Are there any automatic methods that can improve the accuracy of the fault space? We observe that the accuracy of the fault space affects the effectiveness and efficiency of search-based APR techniques, e.g., the failure rate of GenProg could be as high as $60\%$ when the real fix location is ranked lower than 10 even though the correct patch is in the search space. Besides, GenProg is able to find more correct patches and with fewer trials when given a fault space with a higher accuracy. We also find that the negative mutation coverage, which is designed in this study to measure the capability of a test suite to kill the mutants created on the statements executed by failing tests, is the most indicative measurement to estimate the efficiency of search-based APR. Finally, we confirm that automated generated test cases can help improve the accuracy of fault spaces, and further improve the performance of search-based APR techniques.

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 "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"
    - "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"
    - "arja automated repair of java programs via multi objective genetic programming"
    - "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-3 (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"
    - "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"
    - "attention please consider mockito when evaluating newly proposed automated program repair techniques"
    - "an analysis of the search spaces for generate and validate patch generation systems"

Connected through hyperedge HE-4 (induced by source paper "ultra large repair search space with automatically mined templates the cardumen mode of astor"):
  with members:
    - "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 repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "dynamic patch generation for null pointer exceptions using metaprogramming"
    - "test case generation for program repair a study of feasibility and effectiveness"
    - "precise condition synthesis for program repair"
    - "an analysis of the search spaces for generate and validate patch generation systems"

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"
    - "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-6 (induced by source paper "attention please consider mockito when evaluating newly proposed automated program repair techniques"):
  with members:
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "multi location program repair strategies learned from past successful experience"
    - "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"
    - "dynamic patch generation for null pointer exceptions using metaprogramming"
    - "precise condition synthesis for program repair"
    - "mining software repair models for reasoning on the search space of automated program fixing"

Connected through hyperedge HE-7 (induced by source paper "arja automated repair of java programs via multi objective genetic programming"):
  with members:
    - "automatic software repair a bibliography"
    - "automatic repair of real bugs in java a large scale experiment on the defects4j dataset"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "test case generation for program repair a study of feasibility and effectiveness"
    - "precise condition synthesis for program repair"
    - "an analysis of the search spaces for generate and validate patch generation systems"
    - "mining software repair models for reasoning on the search space of automated program fixing"
    - "a critical review of automatic patch generation learned from human written patches essay on the problem statement and the evaluation of automatic software 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)