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: "are query based ontology debuggers really helping knowledge engineers"
  Abstract: Real-world semantic or knowledge-based systems, e.g., in the biomedical domain, can become large and complex. Tool support for the localization and repair of faults within knowledge bases of such systems can therefore be essential for their practical success. Correspondingly, a number of knowledge base debugging approaches, in particular for ontology-based systems, were proposed throughout recent years. Query-based debugging is a comparably recent interactive approach that localizes the true cause of an observed problem by asking knowledge engineers a series of questions. Concrete implementations of this approach exist, such as the OntoDebug plug-in for the ontology editor Protege. #R##N#To validate that a newly proposed method is favorable over an existing one, researchers often rely on simulation-based comparisons. Such an evaluation approach however has certain limitations and often cannot fully inform us about a method's true usefulness. We therefore conducted different user studies to assess the practical value of query-based ontology debugging. One main insight from the studies is that the considered interactive approach is indeed more efficient than an alternative algorithmic debugging based on test cases. We also observed that users frequently made errors in the process, which highlights the importance of a careful design of the queries that users need to answer.

Connected through hyperedge HE-1 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof"):
  with members:
    - "a generally applicable highly scalable measurement computation and optimization approach to sequential model based diagnosis"
    - "inexpensive cost optimized measurement proposal for sequential model based diagnosis"
    - "towards better response times and higher quality queries in interactive knowledge base debugging"
    - "interactive debugging of knowledge bases"
    - "rio minimizing user interaction in ontology debugging"
    - "interactive ontology debugging two query strategies for efficient fault localization"
    - "a scalable algorithm for minimal unsatisfiable core extraction"

Connected through hyperedge HE-2 (induced by source paper "on expert behaviors and question types for efficient query based ontology fault localization"):
  with members:
    - "interactive debugging of knowledge bases"
    - "interactive ontology debugging two query strategies for efficient fault localization"

Connected through hyperedge HE-3 (induced by source paper "a new expert questioning approach to more efficient fault localization in ontologies"):
  with members:
    - "a generally applicable highly scalable measurement computation and optimization approach to sequential model based diagnosis"
    - "inexpensive cost optimized measurement proposal for sequential model based diagnosis"
    - "towards better response times and higher quality queries in interactive knowledge base debugging"
    - "interactive debugging of knowledge bases"
    - "rio minimizing user interaction in ontology debugging"
    - "interactive ontology debugging two query strategies for efficient fault localization"

Connected through hyperedge HE-4 (induced by source paper "towards optimizing reiter s hs tree for sequential diagnosis"):
  with members:
    - "interactive debugging of knowledge bases"
    - "rio minimizing user interaction in ontology debugging"
    - "interactive ontology debugging two query strategies for efficient fault localization"

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.AI(Artificial Intelligence)