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 debugging of knowledge bases"
  Abstract: Many AI applications rely on knowledge about a relevant real-world domain that is encoded by means of some logical knowledge base (KB). The most essential benefit of logical KBs is the opportunity to perform automatic reasoning to derive implicit knowledge or to answer complex queries about the modeled domain. The feasibility of meaningful reasoning requires KBs to meet some minimal quality criteria such as logical consistency. Without adequate tool assistance, the task of resolving violated quality criteria in KBs can be extremely tough even for domain experts, especially when the problematic KB includes a large number of logical formulas or comprises complicated logical formalisms. #R##N#Published non-interactive debugging systems often cannot localize all possible faults (incompleteness), suggest the deletion or modification of unnecessarily large parts of the KB (non-minimality), return incorrect solutions which lead to a repaired KB not satisfying the imposed quality requirements (unsoundness) or suffer from poor scalability due to the inherent complexity of the KB debugging problem. Even if a system is complete and sound and considers only minimal solutions, there are generally exponentially many solution candidates to select one from. However, any two repaired KBs obtained from these candidates differ in their semantics in terms of entailments and non-entailments. Selection of just any of these repaired KBs might result in unexpected entailments, the loss of desired entailments or unwanted changes to the KB. #R##N#This work proposes complete, sound and optimal methods for the interactive debugging of KBs that suggest the one (minimally invasive) error correction of the faulty KB that yields a repaired KB with exactly the intended semantics. Users, e.g. domain experts, are involved in the debugging process by answering automatically generated queries about the intended domain.

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"
    - "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:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "interactive ontology debugging two query strategies for efficient fault localization"

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"
    - "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-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"
    - "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 "towards optimizing reiter s hs tree for sequential diagnosis"):
  with members:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "rio minimizing user interaction in ontology debugging"
    - "interactive ontology debugging two query strategies for efficient fault localization"

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"
    - "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 "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"
    - "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-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"
    - "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)