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: "Simultaneous Learning of Control Laws and Local Environment Representations for Intelligent Navigation Robots"
  Abstract: Two issues of an intelligent navigation robot have been addressed in this work. First is the robot's ability to learn a representation of the local environment and use this representation to identify which local environment it is in. This is done by first extracting features from the sensors which are more informative than just distances of obstacles in various directions. Using these features a reduced ring representation (RRR) of the local environment is derived. As the robot navigates, it learns the RRR signatures of all the new environment types it encounters. For purpose of identification, a ring matching criteria is proposed where the robot tries to match the RRR from the sensory input to one of the RRRs in its library. The second issue addressed is that of learning hill climbing control laws in the local environments. Unlike conventional neuro-controllers, a reinforcement learning framework, where the robot first learns a model of the environment and then learns the control law in terms of a neural network is proposed here. The reinforcement function is generated from the sensory inputs of the robot before and after a control action is taken. Three key results shown in this work are that (1) The robot is able to build its library of RRR signatures perfectly even with significant sensor noise for eight different local environ-mets, (2) It was able to identify its local environment with an accuracy of more than 96%, once the library is build, and (3) the robot was able to learn adequate hill climbing control laws which take it to the distinctive state of the local environment for five different environment types.

Connected through hyperedge HE-1 (induced by source paper "On-Line Adaptation of a Signal Predistorter through Dual Reinforcement Learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Evolutionary Learning of the Crossover Operator"

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