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: "factored particles for scalable monitoring"
  Abstract: Exact monitoring in dynamic Bayesian networks is intractable, so approximate algorithms are necessary. This paper presents a new family of approximate monitoring algorithms that combine the best qualities of the particle filtering and Boyen-Koller methods. Our algorithms maintain an approximate representation the belief state in the form of sets of factored particles, that correspond to samples of clusters of state variables. Empirical results show that our algorithms outperform both ordinary particle filtering and the Boyen-Koller algorithm on large systems.

Connected through hyperedge HE-1 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-2 (induced by source paper "relational dynamic bayesian networks"):
  with members:
    - "loglinear models for first order probabilistic reasoning"
    - "tractable inference for complex stochastic processes"
    - "context specific independence in bayesian networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"
    - "relational bayesian networks"
    - "the ff planning system fast plan generation through heuristic search"
    - "bayesian logic programs"

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)