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 "understanding the quickxplain algorithm simple explanation and formal proof"):
  Abstract: In his seminal paper of 2004, Ulrich Junker proposed the QuickXPlain algorithm, which provides a divide-and-conquer computation strategy to find within a given set an irreducible subset with a particular (monotone) property. Beside its original application in the domain of constraint satisfaction problems, the algorithm has since then found widespread adoption in areas as different as model-based diagnosis, recommender systems, verification, or the Semantic Web. This popularity is due to the frequent occurrence of the problem of finding irreducible subsets on the one hand, and to QuickXPlain's general applicability and favorable computational complexity on the other hand. #R##N#However, although (we regularly experience) people are having a hard time understanding QuickXPlain and seeing why it works correctly, a proof of correctness of the algorithm has never been published. This is what we account for in this work, by explaining QuickXPlain in a novel tried and tested way and by presenting an intelligible formal proof of it. Apart from showing the correctness of the algorithm and excluding the later detection of errors (proof and trust effect), the added value of the availability of a formal proof is, e.g., (i) that the workings of the algorithm often become completely clear only after studying, verifying and comprehending the proof (didactic effect), (ii) the shown proof methodology can be used as a guidance for proving other recursive algorithms (transfer effect), and (iii) the possibility of providing "gapless" correctness proofs of systems that rely on (results computed by) QuickXPlain, such as numerous model-based debuggers (completeness effect).

Includes members:
  Member "are query based ontology debuggers really helping knowledge engineers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on expert behaviors and question types for efficient query based ontology fault localization")
  Member "a generally applicable highly scalable measurement computation and optimization approach to sequential model based diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a new expert questioning approach to more efficient fault localization in ontologies")
  Member "inexpensive cost optimized measurement proposal for sequential model based diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "are query based ontology debuggers really helping knowledge engineers")
  Member "towards better response times and higher quality queries in interactive knowledge base debugging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are query based ontology debuggers really helping knowledge engineers")
  Member "interactive debugging of knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on expert behaviors and question types for efficient query based ontology fault localization")
  Member "rio minimizing user interaction in ontology debugging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "are query based ontology debuggers really helping knowledge engineers")
  Member "interactive ontology debugging two query strategies for efficient fault localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on expert behaviors and question types for efficient query based ontology fault localization")
  Member "a scalable algorithm for minimal unsatisfiable core extraction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "conquering the extensional scalability problem for value flow analysis frameworks")

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.AI(Artificial Intelligence)