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 "state space abstraction for anytime evaluation of probabilistic networks"):
  Abstract: One important factor determining the computational complexity of evaluating a probabilistic network is the cardinality of the state spaces of the nodes. By varying the granularity of the state spaces, one can trade off accuracy in the result for computational efficiency. We present an anytime procedure for approximate evaluation of probabilistic networks based on this idea. On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases. This suggests that state-space abstraction is one more useful control parameter for designing real-time probabilistic reasoners.

Includes members:
  Member "utility based abstraction and categorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "approximations in bayesian belief universe for knowledge based systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "using tree decomposable structures to approximate belief networks":
    also connected through other hyperedges:
      (none)
  Member "bounded conditioning flexible inference for decisions under scarce resources":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using qualitative relationships for bounding probability distributions")
  Member "ideal reformulation of belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "metareasoning for planning under uncertainty")
  Member "refinement and coarsening of bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic network updating techniques for diagnostic reasoning")
  Member "ideal a software package for analysis of influence diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic network updating techniques for diagnostic reasoning")
  Member "incremental probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using qualitative relationships for bounding probability distributions")

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)