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: "interactive ontology debugging two query strategies for efficient fault localization"
  Abstract: Effective debugging of ontologies is an important prerequisite for their broad application, especially in areas that rely on everyday users to create and maintain knowledge bases, such as the Semantic Web. In such systems ontologies capture formalized vocabularies of terms shared by its users. However in many cases users have different local views of the domain, i.e. of the context in which a given term is used. Inappropriate usage of terms together with natural complications when formulating and understanding logical descriptions may result in faulty ontologies. Recent ontology debugging approaches use diagnosis methods to identify causes of the faults. In most debugging scenarios these methods return many alternative diagnoses, thus placing the burden of fault localization on the user. This paper demonstrates how the target diagnosis can be identified by performing a sequence of observations, that is, by querying an oracle about entailments of the target ontology. To identify the best query we propose two query selection strategies: a simple "split-in-half" strategy and an entropy-based strategy. The latter allows knowledge about typical user errors to be exploited to minimize the number of queries. Our evaluation showed that the entropy-based method significantly reduces the number of required queries compared to the "split-in-half" approach. We experimented with different probability distributions of user errors and different qualities of the a-priori probabilities. Our measurements demonstrated the superiority of entropy-based query selection even in cases where all fault probabilities are equal, i.e. where no information about typical user errors is available.

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"
    - "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"
    - "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:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "interactive debugging of knowledge bases"

Connected through hyperedge HE-3 (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"
    - "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"
    - "catching the ouroboros on debugging non ground answer set programs"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "towards optimizing reiter s hs tree for sequential diagnosis"):
  with members:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "interactive debugging of knowledge bases"
    - "rio minimizing user interaction in ontology debugging"

Connected through hyperedge HE-6 (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"
    - "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"

Connected through hyperedge HE-7 (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"
    - "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"
    - "sequential diagnosis by abstraction"

Connected through hyperedge HE-8 (induced by source paper "on the discrimination power and effective utilization of active learning measures in version space search"):
  with members:
    - "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"

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)