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: "reasoning about probabilities in dynamic systems using goal regression"
  Abstract: Reasoning about degrees of belief in uncertain dynamic worlds is fundamental to many applications, such as robotics and planning, where actions modify state properties and sensors provide measurements, both of which are prone to noise. With the exception of limited cases such as Gaussian processes over linear phenomena, belief state evolution can be complex and hard to reason with in a general way. This paper proposes a framework with new results that allows the reduction of subjective probabilities after sensing and acting to questions about the initial state only. We build on an expressive probabilistic first-order logical account by Bacchus, Halpern and Levesque, resulting in a methodology that, in principle, can be coupled with a variety of existing inference solutions.

Connected through hyperedge HE-1 (induced by source paper "reasoning about discrete and continuous noisy sensors and effectors in dynamical systems"):
  with members:
    - "robot location estimation in the situation calculus"
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "probabilistic reasoning with answer sets"
    - "modelling mixed discrete continuous domains for planning"
    - "decision theoretic planning structural assumptions and computational leverage"
    - "reasoning about noisy sensors and effectors in the situation calculus"

Connected through hyperedge HE-2 (induced by source paper "robot location estimation in the situation calculus"):
  with members:
    - "action networks a framework for reasoning about actions and change under uncertainty"
    - "monitoring a complez physical system using a hybrid dynamic bayes net"
    - "probabilistic reasoning with answer sets"
    - "bayesian logic programs"
    - "reasoning about noisy sensors and effectors in the situation calculus"

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)