human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent groups of papers sharing a common citing paper.

Details of the hypergraph:
Center node: "Optimal Navigation in a Probibalistic World"
  Abstract: In this paper, we define and examine two versions of the bridge problem. The first variant of the bridge problem is a determistic model where the agent knows a superset of the transitions and a priori probabilities that those transitions are intact. In the second variant, transitions can break or be fixed with some probability at each time step. These problems are applicable to planning in uncertain domains as well as packet routing in a computer network. We show how an agent can act optimally in these models by reduction to Markov decision processes. We describe methods of solving them but note that these methods are intractable for reasonably sized problems. Finally, we suggest neuro-dynamic programming as a method of value function approximation for these types of models.

Connected through hyperedge HE-1 (induced by source paper "Planning and Acting in Partially Observable Stochastic Domains"):
  with members:
    - "A Cognitive Model of Learning to Navigate"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"

Connected through hyperedge HE-2 (induced by source paper "On the Complexity of Solving Markov Decision Problems"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Tight Performance Bounds on Greedy Policies Based on Imperfect Value Functions"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"

Question: Please tell me which class the center node belongs to. The 7 classes are: Neural_Networks, Reinforcement_Learning, Probabilistic_Methods, Theory, Genetic_Algorithms, Case_Based, Rule_Learning. Directly output the class name.

gpt: Reinforcement_Learning