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: "alleviating patch overfitting with automatic test generation a study of feasibility and effectiveness for the nopol repair system"
  Abstract: Among the many different kinds of program repair techniques, one widely studied family of techniques is called test suite based repair. However, test suites are in essence input-output specifications and are thus typically inadequate for completely specifying the expected behavior of the program under repair. Consequently, the patches generated by test suite based repair techniques can just overfit to the used test suite, and fail to generalize to other tests. We deeply analyze the overfitting problem in program repair and give a classification of this problem. This classification will help the community to better understand and design techniques to defeat the overfitting problem. We further propose and evaluate an approach called UnsatGuided, which aims to alleviate the overfitting problem for synthesis-based repair techniques with automatic test case generation. The approach uses additional automatically generated tests to strengthen the repair constraint used by synthesis-based repair techniques. We analyze the effectiveness of UnsatGuided: 1) analytically with respect to alleviating two different kinds of overfitting issues; 2) empirically based on an experiment over the 224 bugs of the Defects4J repository. The main result is that automatic test generation is effective in alleviating one kind of overfitting, issue–regression introduction, but due to oracle problem, has minimal positive impact on alleviating the other kind of overfitting issue–incomplete fixing.

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"
    - "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 "ifixr bug report driven program repair"):
  with members:
    - "d c a divide and conquer approach to ir based bug localization"
    - "tbar revisiting template based automated program repair"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "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-3 (induced by source paper "learning the relation between code features and code transforms with structured prediction"):
  with members:
    - "sequencer sequence to sequence learning for end to end program repair"
    - "towards an automated approach for bug fix pattern detection"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "automatic software repair a bibliography"
    - "path based function embedding and its application to specification mining"
    - "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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "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"
    - "arja automated repair of java programs via multi objective genetic programming"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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-7 (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"
    - "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 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"

Connected through hyperedge HE-8 (induced by source paper "validation of automatically generated patches an appetizer"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "getafix learning to fix bugs automatically"
    - "predicting metamorphic relation for matrix calculation programs"
    - "using semi supervised learning for predicting metamorphic relations"
    - "how to design a program repair bot insights from the repairnator project"
    - "identifying patch correctness in test based program repair"
    - "nopol automatic repair of conditional statement bugs in java programs"
    - "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)