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: "rio minimizing user interaction in ontology debugging"
  Abstract: Efficient ontology debugging is a cornerstone for many activities in the context of the Semantic Web, especially when automatic tools produce (parts of) ontologies such as in the field of ontology matching. The best currently known interactive debugging systems rely upon some meta information in terms of fault probabilities, which can speed up the debugging procedure in the good case, but can also have negative impact on the performance in the bad case. The problem is that assessment of the meta information is only possible a-posteriori. Consequently, as long as the actual fault is unknown, there is always some risk of suboptimal interactive diagnoses discrimination. As an alternative, one might prefer to rely on a tool which pursues a no-risk strategy. In this case, however, possibly well-chosen meta information cannot be exploited, resulting again in inefficient debugging actions. In this work we present a reinforcement learning strategy that continuously adapts its behavior depending on the performance achieved and minimizes the risk of using low-quality meta information. Therefore, this method is suitable for application scenarios where reliable a-priori fault estimates are difficult to obtain. Using problematic ontologies in the field of ontology matching, we show that the proposed risk-aware query strategy outperforms both active learning approaches and no-risk strategies on average in terms of required amount of user interaction.

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"
    - "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"
    - "towards better response times and higher quality queries in interactive knowledge base debugging"
    - "interactive debugging of knowledge bases"
    - "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"
    - "towards better response times and higher quality queries in interactive knowledge base debugging"
    - "interactive debugging of knowledge bases"
    - "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:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "interactive debugging of knowledge bases"
    - "interactive ontology debugging two query strategies for efficient fault localization"

Connected through hyperedge HE-5 (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"
    - "interactive ontology debugging two query strategies for efficient fault localization"

Connected through hyperedge HE-6 (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"
    - "interactive ontology debugging two query strategies for efficient fault localization"
    - "sequential diagnosis by abstraction"

Connected through hyperedge HE-7 (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"
    - "interactive ontology debugging two query strategies for efficient fault localization"

Connected through hyperedge HE-8 (induced by source paper "scalable computation of optimized queries for sequential diagnosis"):
  with members:
    - "towards better response times and higher quality queries in interactive knowledge base debugging"
    - "interactive debugging of knowledge bases"
    - "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)