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 "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"):
  Abstract: Properly benchmarking Automated Program Repair (APR) systems should contribute to the development and adoption of the research outputs by practitioners. To that end, the research community must ensure that it reaches significant milestones by reliably comparing state-of-the-art tools for a better understanding of their strengths and weaknesses. In this work, we identify and investigate a practical bias caused by the fault localization (FL) step in a repair pipeline. We propose to highlight the different fault localization configurations used in the literature, and their impact on APR systems when applied to the Defects4J benchmark. Then, we explore the performance variations that can be achieved by `tweaking' the FL step. Eventually, we expect to create a new momentum for (1) full disclosure of APR experimental procedures with respect to FL, (2) realistic expectations of repairing bugs in Defects4J, as well as (3) reliable performance comparison among the state-of-the-art APR systems, and against the baseline performance results of our thoroughly assessed kPAR repair tool. Our main findings include: (a) only a subset of Defects4J bugs can be currently localized by commonly-used FL techniques; (b) current practice of comparing state-of-the-art APR systems (i.e., counting the number of fixed bugs) is potentially misleading due to the bias of FL configurations; and (c) APR authors do not properly qualify their performance achievement with respect to the different tuning parameters implemented in APR systems.

Includes members:
  Member "fixminer mining relevant fix patterns for automated program repair":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair")
  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 "arja automated repair of java programs via multi objective genetic programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "an empirical analysis of the influence of fault space on search based automated program repair":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tbar revisiting template based automated program repair")
  Member "identifying patch correctness in test based program repair":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "e apr mapping the effectiveness of automated program repair")
  Member "impact of tool support in patch construction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to catch security patches")
  Member "automated correction for syntax errors in programming assignments using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "big code big vocabulary open vocabulary models for source code")

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)