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: "arja automated repair of java programs via multi objective genetic programming"
  Abstract: Recent empirical studies show that the performance of GenProg is not satisfactory, particularly for Java. In this paper, we propose ARJA, a new GP based repair approach for automated repair of Java programs. To be specific, we present a novel lower-granularity patch representation that properly decouples the search subspaces of likely-buggy locations, operation types and potential fix ingredients, enabling GP to explore the search space more effectively. Based on this new representation, we formulate automated program repair as a multi-objective search problem and use NSGA-II to look for simpler repairs. To reduce the computational effort and search space, we introduce a test filtering procedure that can speed up the fitness evaluation of GP and three types of rules that can be applied to avoid unnecessary manipulations of the code. Moreover, we also propose a type matching strategy that can create new potential fix ingredients by exploiting the syntactic patterns of the existing statements. We conduct a large-scale empirical evaluation of ARJA along with its variants on both seeded bugs and real-world bugs in comparison with several state-of-the-art repair approaches. Our results verify the effectiveness and efficiency of the search mechanisms employed in ARJA and also show its superiority over the other approaches. In particular, compared to jGenProg (an implementation of GenProg for Java), an ARJA version fully following the redundancy assumption can generate a test-suite adequate patch for more than twice the number of bugs (from 27 to 59), and a correct patch for nearly four times of the number (from 5 to 18), on 224 real-world bugs considered in Defects4J. Furthermore, ARJA is able to correctly fix several real multi-location bugs that are hard to be repaired by most of the existing repair approaches.

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"
    - "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"
    - "identifying patch correctness in test based program repair"

Connected through hyperedge HE-2 (induced by source paper "search based repair of deep neural networks"):
  with members:
    - "harnessing evolution for multi hunk program repair"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "a survey on data collection for machine learning a big data ai integration perspective"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "a survey on deep learning in medical image analysis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-3 (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"
    - "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"
    - "dissection of a bug dataset anatomy of 395 patches from defects4j"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "a comprehensive study of automatic program repair on the quixbugs benchmark"):
  with members:
    - "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
    - "automatic software repair a bibliography"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "precise condition synthesis for program repair"
    - "npefix automatic runtime repair of null pointer exceptions in java"
    - "automatic repair of real bugs an experience report on the defects4j dataset"
    - "a critical review of automatic patch generation learned from human written patches essay on the problem statement and the evaluation of automatic software repair"

Connected through hyperedge HE-6 (induced by source paper "a journey among java neutral program variants"):
  with members:
    - "source code optimization using equivalent mutants"
    - "darwinian data structure selection"
    - "correctness attraction a study of stability of software behavior under runtime perturbation"
    - "the multiple facets of software diversity recent developments in year 2000 and beyond"
    - "tailored source code transformations to synthesize computationally diverse program variants"
    - "software mutational robustness"

Connected through hyperedge HE-7 (induced by source paper "flakime laboratory controlled test flakiness impact assessment a case study on mutation testing and program repair"):
  with members:
    - "empirical analysis of factors and their effect on test flakiness practitioners perceptions"

Connected through hyperedge HE-8 (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"
    - "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"

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)