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 "factored probabilistic belief tracking"):
  Abstract: The problem of belief tracking in the presence of stochastic actions and observations is pervasive and yet computationally intractable. In this work we show however that probabilistic beliefs can be maintained in factored form exactly and efficiently across a number of causally closed beams, when the state variables that appear in more than one beam obey a form of backward determinism. Since computing marginals from the factors is still computationally intractable in general, and variables appearing in several beams are not always backward-deterministic, the basic formulation is extended with two approximations: forms of belief propagation for computing marginals from factors, and sampling of non-backward-deterministic variables for making such variables backward-deterministic given their sampled history. Unlike, Rao-Blackwellized particle-filtering, the sampling is not used for making inference tractable but for making the factorization sound. The resulting algorithm involves sampling and belief propagation or just one of them as determined by the structure of the model.

Includes members:
  Member "stochastic simulation algorithms for dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "review fight sample degeneracy and impoverishment in particle filters a review of intelligent approaches")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abstracting probabilistic models a logical perspective")
  Member "a general non probabilistic theory of inductive reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "defaults and infinitesimals defeasible inference by nonarchimedean entropy maximization")
  Member "the factored frontier algorithm for approximate inference in dbns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")
  Member "rao blackwellised particle filtering for dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "poserbpf a rao blackwellized particle filter for 6d object pose tracking")
  Member "on the relation between kappa calculus and probabilistic reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "query dags a practical paradigm for implementing belief network inference")
  Member "a simple insight into iterative belief propagation s success":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "modeling transportation routines using hybrid dynamic mixed networks")

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)