human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "precise condition synthesis for program repair"):
  Abstract: Due to the difficulty of repairing defect, many research efforts have been devoted into automatic defect repair. Given a buggy program that fails some test cases, a typical automatic repair technique tries to modify the program to make all tests pass. However, since the test suites in real world projects are usually insufficient, aiming at passing the test suites often leads to incorrect patches. #R##N#In this paper we aim to produce precise patches, that is, any patch we produce has a relatively high probability to be correct. More concretely, we focus on condition synthesis, which was shown to be able to repair more than half of the defects in existing approaches. Our key insight is threefold. First, it is important to know what variables in a local context should be used in an "if" condition, and we propose a sorting method based on the dependency relations between variables. Second, we observe that the API document can be used to guide the repair process, and propose document analysis technique to further filter the variables. Third, it is important to know what predicates should be performed on the set of variables, and we propose to mine a set of frequently used predicates in similar contexts from existing projects. #R##N#We develop a novel program repair system, ACS, that could generate precise conditions at faulty locations. Furthermore, given the generated conditions are very precise, we can perform a repair operation that is previously deemed to be too overfitting: directly returning the test oracle to repair the defect. Using our approach, we successfully repaired 18 defects on four projects of Defects4J, which is the largest number of fully automatically repaired defects reported on the dataset so far. More importantly, the precision of our approach in the evaluation is 78.3%, which is significantly higher than previous approaches, which are usually less than 40%.

Includes members:
  Member "automatic software repair a bibliography":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a recurrent neural network based patch recommender for linux kernel bugs")
  Member "automatic repair of real bugs in java a large scale experiment on the defects4j dataset":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "nopol automatic repair of conditional statement bugs in java programs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "an analysis of the search spaces for generate and validate patch generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair")
  Member "automatic repair of real bugs an experience report on the defects4j dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comprehensive study of automatic program repair on the quixbugs benchmark")
  Member "a critical review of automatic patch generation learned from human written patches essay on the problem statement and the evaluation of automatic software repair":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ifixr bug report driven program repair")
  Member "automated fixing of programs with contracts":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "validation of automatically generated patches an appetizer")
  Member "minthint automated synthesis of repair hints":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to generate corrective patches using neural machine translation")

Question: Please tell me which class the source paper 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)