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: "action networks a framework for reasoning about actions and change under uncertainty"
  Abstract: This work proposes action networks as a semantically well-founded framework for reasoning about actions and change under uncertainty. Action networks add two primitives to probabilistic causal networks: controllable variables and persistent variables. Controllable variables allow the representation of actions as directly setting the value of specific events in the domain, subject to preconditions. Persistent variables provide a canonical model of persistence according to which both the state of a variable and the causal mechanism dictating its value persist over time unless intervened upon by an action (or its consequences). Action networks also allow different methods for quantifying the uncertainty in causal relationships, which go beyond traditional probabilistic quantification. This paper describes both recent results and work in progress.

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"
    - "reasoning about probabilities in dynamic systems using goal regression"
    - "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:
    - "reasoning about probabilities in dynamic systems using goal regression"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a structurally and temporally extended bayesian belief network model definitions properties and modeling techniques"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "properties of bayesian belief network learning algorithms"
    - "probabilistic temporal reasoning with endogenous change"
    - "forecasting sleep apnea with dynamic network models"

Connected through hyperedge HE-4 (induced by source paper "decision theoretic troubleshooting a framework for repair and experiment"):
  with members:
    - "a generalization of the noisy or model"
    - "from conditional oughts to qualitative decision theory"
    - "decision theoretic foundations for causal reasoning"

Connected through hyperedge HE-5 (induced by source paper "exploiting system hierarchy to compute repair plans in probabilistic model based diagnosis"):
  with members:
    - "counterfactual probabilities computational methods bounds and applications"
    - "knowledge based decision model construction for hierarchical diagnosis a preliminary report"
    - "a polynomial algorithm for computing the optimal repair strategy in a system with independent component failures"

Connected through hyperedge HE-6 (induced by source paper "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"):
  with members:
    - "planning with external events"
    - "generating bayesian networks from probability logic knowledge bases"

Connected through hyperedge HE-7 (induced by source paper "a structured probabilistic representation of action"):
  with members:
    - "causality in bayesian belief networks"
    - "epsilon safe planning"

Connected through hyperedge HE-8 (induced by source paper "conditioning methods for exact and approximate inference in causal networks"):
  with members:
    - "fast belief update using order of magnitude probabilities"

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)