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 "active tuples based scheme for bounding posterior beliefs"):
  Abstract: The paper presents a scheme for computing lower and upper bounds on the posterior marginals in Bayesian networks with discrete variables. Its power lies in its ability to use any available scheme that bounds the probability of evidence or posterior marginals and enhance its performance in an anytime manner. The scheme uses the cutset conditioning principle to tighten existing bounding schemes and to facilitate anytime behavior, utilizing a fixed number of cutset tuples. The accuracy of the bounds improves as the number of used cutset tuples increases and so does the computation time. We demonstrate empirically the value of our scheme for bounding posterior marginals and probability of evidence using a variant of the bound propagation algorithm as a plug-in scheme.

Includes members:
  Member "context specific approximation in probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "representing and combining partially specified cpts")
  Member "large deviation methods for approximate probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mean field methods for a special class of belief networks")
  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 "knowledge engineering for large belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "a sufficiently fast algorithm for finding close to optimal junction trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables")
  Member "accuracy bounds for belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anytime exact belief propagation")
  Member "approximate decomposition a method for bounding and estimating probabilistic and deterministic queries":
    also connected through other hyperedges:
      (none)
  Member "an empirical study of w cutset sampling for bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cutset sampling with likelihood weighting")

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)