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: "structured reachability analysis for markov decision processes"
  Abstract: Recent research in decision theoretic planning has focussed on making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structured reachability analysis of MDPs that are suitable when an initial state (or set of states) is known. Using compact, structured representations of MDPs (e.g., Bayesian networks), our methods, which vary in the tradeoff between complexity and accuracy, produce structured descriptions of (estimated) reachable states that can be used to eliminate variables or variable values from the problem description, reducing the size of the MDP and making it easier to solve. One contribution of our work is the extension of ideas from GRAPHPLAN to deal with the distributed nature of action representations typically embodied within Bayes nets and the problem of correlated action effects. We also demonstrate that our algorithm can be made more complete by using k-ary constraints instead of binary constraints. Another contribution is the illustration of how the compact representation of reachability constraints can be exploited by several existing (exact and approximate) abstraction algorithms for MDPs.

Connected through hyperedge HE-1 (induced by source paper "reachability and differential based heuristics for solving markov decision processes"):
  with members:
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "accelerating goal directed reinforcement learning by model characterization"):
  with members:
    - "reachability and differential based heuristics for solving markov decision processes"
    - "combining model based and model free updates for trajectory centric reinforcement learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "mbmf model based priors for model free reinforcement learning"
    - "task based end to end model learning in stochastic optimization"
    - "trust region policy optimization"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "heuristic search value iteration for pomdps"):
  with members:
    - "integrating planning and execution in stochastic domains"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "spudd stochastic planning using decision diagrams"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-4 (induced by source paper "cost sensitive reachability heuristics for handling state uncertainty"):
  with members:
    - "optimal limited contingency planning"
    - "the 3rd international planning competition results and analysis"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-5 (induced by source paper "restricted value iteration theory and algorithms"):
  with members:
    - "flexible decomposition algorithms for weakly coupled markov decision problems"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "policy iteration for factored mdps"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"
    - "the computational complexity of probabilistic planning"
    - "a model approximation scheme for planning in partially observable stochastic domains"

Connected through hyperedge HE-6 (induced by source paper "decision theoretic planning structural assumptions and computational leverage"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "flexible decomposition algorithms for weakly coupled markov decision problems"
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "integrating planning and execution in stochastic domains"
    - "abstracting probabilistic actions"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"

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)