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: "Planning and Acting in Partially Observable Stochastic Domains"
  Abstract: In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (mdps) and partially observable mdps (pomdps). We then outline a novel algorithm for solving pomdps off line and show how, in some cases, a finite-memory controller can be extracted from the solution to a pomdp. We conclude with a discussion of how our approach relates to previous work, the complexity of finding exact solutions to pomdps, and of some possibilities for finding approximate solutions. Consider the problem of a robot navigating in a large office building. The robot can move from hallway intersection to intersection and can make local observations of its world. Its actions are not completely reliable, however. Sometimes, when it intends to move, it stays where it is or goes too far; sometimes, when it intends to turn, it overshoots. It has similar problems with observation. Sometimes a corridor looks like a corner; sometimes a T-junction looks like an L-junction. How can such an error-plagued robot navigate, even given a map of the corridors? In general, the robot will have to remember something about its history of actions and observations and use this information, together with its knowledge of the underlying dynamics of the world (the map and other information), to maintain an estimate of its location. Many engineering applications follow this approach, using methods like the Kalman filter [18] to maintain a running estimate of the robot's spatial uncertainty, expressed as an ellipsoid or normal distribution in Cartesian space. This approach will not do for our robot, though. Its uncertainty may be discrete: it might be almost certain that it is in the north-east corner of either the fourth or the seventh floors, though it admits a chance that it is on the fifth floor, as well. Then, given an uncertain estimate of its location, the robot has to decide what actions to take. In some cases, it might be sufficient to ignore its uncertainty and take actions that would be appropriate for the most likely location. In other cases, it might be better for

Connected through hyperedge HE-1 (induced by source paper "A Cognitive Model of Learning to Navigate"):
  with members:
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"
    - "Forward models: Supervised learning with a distal teacher"
    - "Reinforcement Learning with Soft State Aggregation"
    - "A Comparison of Action Selection Learning Methods"

Connected through hyperedge HE-2 (induced by source paper "Active Gesture Recognition using Partially Observable Markov Decision Processes"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Learning networks for face analysis and synthesis"
    - "Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems"

Connected through hyperedge HE-3 (induced by source paper "Optimal Navigation in a Probibalistic World"):
  with members:
    - "On the Complexity of Solving Markov Decision Problems"

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