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: "towards better response times and higher quality queries in interactive knowledge base debugging"
  Abstract: Many AI applications rely on knowledge encoded in a locigal knowledge base (KB). The most essential benefit of such logical KBs is the opportunity to perform automatic reasoning which however requires a KB to meet some minimal quality criteria such as consistency. Without adequate tool assistance, the task of resolving such violated quality criteria in a KB can be extremely hard, especially when the problematic KB is large and complex. To this end, interactive KB debuggers have been introduced which ask a user queries whether certain statements must or must not hold in the intended domain. The given answers help to gradually restrict the search space for KB repairs. #R##N#Existing interactive debuggers often rely on a pool-based strategy for query computation. A pool of query candidates is precomputed, from which the best candidate according to some query quality criterion is selected to be shown to the user. This often leads to the generation of many unnecessary query candidates and thus to a high number of expensive calls to logical reasoning services. We tackle this issue by an in-depth mathematical analysis of diverse real-valued active learning query selection measures in order to determine qualitative criteria that make a query favorable. These criteria are the key to devising efficient heuristic query search methods. The proposed methods enable for the first time a completely reasoner-free query generation for interactive KB debugging while at the same time guaranteeing optimality conditions, e.g. minimal cardinality or best understandability for the user, of the generated query that existing methods cannot realize. #R##N#Further, we study different relations between active learning measures. The obtained picture gives a hint about which measures are more favorable in which situation or which measures always lead to the same outcomes, based on given types of queries.

Connected through hyperedge HE-1 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof"):
  with members:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "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"
    - "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 "are query based ontology debuggers really helping knowledge engineers"):
  with members:
    - "consequence based axiom pinpointing"
    - "inexpensive cost optimized measurement proposal for sequential model based diagnosis"
    - "interactive debugging of knowledge bases"
    - "rio minimizing user interaction in ontology debugging"
    - "interactive ontology debugging two query strategies for efficient fault localization"
    - "catching the ouroboros on debugging non ground answer set programs"

Connected through hyperedge HE-3 (induced by source paper "a new expert questioning approach to more efficient fault localization in ontologies"):
  with members:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "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"
    - "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 "evaluating active learning heuristics for sequential diagnosis"):
  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"
    - "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-5 (induced by source paper "a generally applicable highly scalable measurement computation and optimization approach to sequential model based diagnosis"):
  with members:
    - "inexpensive cost optimized measurement proposal for sequential model based diagnosis"
    - "interactive debugging of knowledge bases"
    - "rio minimizing user interaction in ontology debugging"
    - "interactive ontology debugging two query strategies for efficient fault localization"
    - "sequential diagnosis by abstraction"

Connected through hyperedge HE-6 (induced by source paper "on the discrimination power and effective utilization of active learning measures in version space search"):
  with members:
    - "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-7 (induced by source paper "scalable computation of optimized queries 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"
    - "sequential diagnosis by abstraction"

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)