human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "inexpensive cost optimized measurement proposal for sequential model based diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof")
  Member "towards better response times and higher quality queries in interactive knowledge base debugging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof")
  Member "interactive debugging of knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof")
  Member "rio minimizing user interaction in ontology debugging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof")
  Member "interactive ontology debugging two query strategies for efficient fault localization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding the quickxplain algorithm simple explanation and formal proof")
  Member "sequential diagnosis by abstraction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scalable computation of optimized queries for sequential diagnosis")

Question: Please tell me which class the source paper 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)