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: "revisiting ssfix for better program repair"
  Abstract: A branch of automated program repair (APR) techniques look at finding and reusing existing code for bug repair. ssFix is one of such techniques that is syntactic search-based: it searches a code database for code fragments that are syntactically similar to the bug context and reuses such retrieved code fragments to produce patches. Using such a syntactic approach, ssFix is relatively lightweight and was shown to outperform many other APR techniques. In this paper, to investigate the true effectiveness of ssFix, we conducted multiple experiments to validate ssFix's built-upon assumption (i.e., to see whether it is often possible to reuse existing code for bug repair) and evaluate its code search and code reuse approaches. Our results show that while the basic idea of ssFix, i.e., reusing existing code for bug repair, is promising, the approaches ssFix uses are not the best and can be significantly improved. We proposed a new repair technique sharpFix which follows ssFix's basic idea but differs in the code search and reuse approaches used. We evaluated sharpFix and ssFix on two bug datasets: Defects4J and Bugs.jar-ELIXIR. The results confirm that sharpFix is an improvement over ssFix. For the Defects4J dataset, sharpFix successfully repaired a total of 36 bugs and outperformed many existing repair techniques in repairing more bugs. For the Bugs.jar-ELIXIR dataset, we compared sharpFix, ssFix, and four other APR techniques, and found that sharpFix has the best repair performance. In essence, the paper shows how effective a syntactic search-based approach can be and what techniques should be used for such an approach.

Connected through hyperedge HE-1 (induced by source paper "the remarkable role of similarity in redundancy based program repair"):
  with members:
    - "astor exploring the design space of generate and validate program repair beyond genprog"
    - "sorting and transforming program repair ingredients via deep learning code similarities"
    - "the codrep machine learning on source code competition"
    - "code2seq generating sequences from structured representations of code"
    - "neural network based graph embedding for cross platform binary code similarity detection"
    - "learning to represent programs with graphs"
    - "precise condition synthesis for program repair"
    - "an analysis of the search spaces for generate and validate patch generation systems"

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)