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: "a generally applicable highly scalable measurement computation and optimization approach to sequential model based diagnosis"
  Abstract: Model-Based Diagnosis deals with the identification of the real cause of a system's malfunction based on a formal system model and observations of the system behavior. When a malfunction is detected, there is usually not enough information available to pinpoint the real cause and one needs to discriminate between multiple fault hypotheses (called diagnoses). To this end, Sequential Diagnosis approaches ask an oracle for additional system measurements. #R##N#This work presents strategies for (optimal) measurement selection in model-based sequential diagnosis. In particular, assuming a set of leading diagnoses being given, we show how queries (sets of measurements) can be computed and optimized along two dimensions: expected number of queries and cost per query. By means of a suitable decoupling of two optimizations and a clever search space reduction the computations are done without any inference engine calls. For the full search space, we give a method requiring only a polynomial number of inferences and show how query properties can be guaranteed which existing methods do not provide. Evaluation results using real-world problems indicate that the new method computes (virtually) optimal queries instantly independently of the size and complexity of the considered diagnosis problems and outperforms equally general methods not exploiting the proposed theory by orders of magnitude.

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"
    - "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 "a new expert questioning approach to more efficient fault localization in ontologies"):
  with members:
    - "are query based ontology debuggers really helping knowledge engineers"
    - "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-3 (induced by source paper "evaluating active learning heuristics for sequential 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"
    - "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)