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: "integrating planning and execution in stochastic domains"
  Abstract: We investigate planning in time-critical domains represented as Markov Decision Processes, showing that search based techniques can be a very powerful method for finding close to optimal plans. To reduce the computational cost of planning in these domains, we execute actions as we construct the plan, and sacrifice optimality by searching to a fixed depth and using a heuristic function to estimate the value of states. Although this paper concentrates on the search algorithm, we also discuss ways of constructing heuristic functions suitable for this approach. Our results show that by interleaving search and execution, close to optimal policies can be found without the computational requirements of other approaches.

Connected through hyperedge HE-1 (induced by source paper "heuristic search value iteration for pomdps"):
  with members:
    - "structured reachability analysis for markov decision processes"
    - "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-2 (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"
    - "abstracting probabilistic actions"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "structured reachability analysis for markov decision processes"
    - "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)