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: "relational dynamic bayesian networks"
  Abstract: Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efciently and accurately is difcult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to rst-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We rst extend the Rao-Blackwellised particle ltering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle ltering. The rst one uses abstraction hierarchies over the predicates to smooth the particle lter’ s estimates. The second employs kernel density estimation with a kernel function specically designed for relational domains. Experiments show these two methods greatly outperform standard particle ltering on the task of assembly plan execution monitoring.

Connected through hyperedge HE-1 (induced by source paper "probabilistic complex event recognition a survey"):
  with members:
    - "going deeper into action recognition a survey"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "a probabilistic logic programming event calculus"
    - "lifted relational neural networks"
    - "complex events recognition under uncertainty in a sensor network"
    - "relational bayesian networks"
    - "location based reasoning about complex multi agent behavior"
    - "a model for reasoning with uncertain rules in event composition systems"

Connected through hyperedge HE-2 (induced by source paper "first order decision diagrams for relational mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "exploiting first order regression in inductive policy selection"
    - "practical linear value approximation techniques for first order mdps"
    - "policy iteration for relational mdps"
    - "approximate linear programming for first order mdps"
    - "flucap a heuristic search planner for first order mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-3 (induced by source paper "learning symbolic models of stochastic domains"):
  with members:
    - "learning bayesian networks with local structure"
    - "tractable inference for complex stochastic processes"
    - "inductive policy selection for first order mdps"
    - "logical hidden markov models"
    - "efficient solution algorithms for factored mdps"

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)