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: "how different is it between machine generated and developer provided patches an empirical study on the correct patches generated by automated program repair techniques"
  Abstract: Background: Over the years, Automated Program Repair (APR) has attracted much attention from both academia and industry since it can reduce the costs in fixing bugs. However, how to assess the patch correctness remains to be an open challenge. Two widely adopted ways to approach this challenge, including manually checking and validating using automated generated tests, are biased (i.e., suffering from subjectivity and low precision respectively). Aim: To address this concern, we propose to conduct an empirical study towards understanding the correct patches that are generated by existing state-of-the-art APR techniques, aiming at providing guidelines for future assessment of patches. Method: To this end, we first present a Literature Review (LR) on the reported correct patches generated by recent techniques on the Defects4J benchmark and collect 177 correct patches after a process of sanity check. We investigate how these machine-generated correct patches achieve semantic equivalence, but syntactic difference compared with developer-provided ones, how these patches distribute in different projects and APR techniques, and how the characteristics of a bug affect the patches generated for it. Results: Our main findings include 1) we do not need to fix bugs exactly like how developers do since we observe that 25.4% (45/177) of the correct patches generated by APR techniques are syntactically different from developer-provided ones; 2) the distribution of machine-generated correct patches diverges for the aspects of Defects4J projects and APR techniques; and 3) APR techniques tend to generate patches that are different from those by developers for bugs with large patch sizes. Conclusion: Our study not only verifies the conclusions from previous studies but also highlights implications for future study towards assessing patch correctness.

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"
    - "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"
    - "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 patch assessment for program repair at scale"):
  with members:
    - "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"
    - "on reliability of patch correctness assessment"

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)