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: "validation of automatically generated patches an appetizer"
  Abstract: In the context of test case based automated program repair (APR), the research community call the patches that pass all the test cases but fail to actually fix the bug test case overfitted patches. Currently, overfitted patches has to be manually inspected by the users. Being a labor intensive activity that hinders widespread adoption of APR tools, automatic validation of APR-generated patches has been the topic of research in recent years. In this paper, we point out the limitations of the existing techniques/methodologies that call for further research, and introduce two promising directions toward effective automatic patch validation: (1) motivated by the relative effectiveness of anti-patterns, we propose to use statistical techniques to avoid the uncomputability of applying some of the anti-pattern rules and automate the technique. Our results show that we achieve at least 57% precision. (2) We present a proposal for a semi-automatic technique that helps the programmers in finding properties of the patched methods and stress testing the patches based on those properties so as to filter out overfitted ones as many as possible.

Connected through hyperedge HE-1 (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"
    - "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)