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: "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
  Abstract: Miller, G. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. The Psychological Review, 63(2):81-97. Schmidhuber, J. (1990b). Towards compositional learning with dynamic neural networks. Technical Report FKI-129-90, Technische Universitat Munchen, Institut fu Informatik. Servan-Schreiber, D., Cleermans, A., and McClelland, J. (1988). Encoding sequential structure in simple recurrent networks. Technical Report CMU-CS-88-183, Carnegie Mellon University, Computer Science Department.

Connected through hyperedge HE-1 (induced by source paper "LEARNING TO AVOID COLLISIONS: A REINFORCEMENT LEARNING PARADIGM FOR MOBILE ROBOT NAVIGATION"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Adaptive state space quantisation: adding and removing neurons"
    - "Adaptive state space quantisation for reinforcement learning of collision-free navigation"

Connected through hyperedge HE-2 (induced by source paper "Learning Controllers for Industrial Robots"):
  with members:
    - "GAL: Networks that grow when they learn and shrink when they forget"
    - "Designing Neural Networks for Adaptive Control"
    - "GA-RBF: A Self-Optimising RBF Network"
    - "FONN: Combining First Order Logic with Connectionist Learning"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Learning Concepts from Sensor Data of a Mobile Robot"

Connected through hyperedge HE-3 (induced by source paper "Learning from undiscounted delayed rewards"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Designing Neural Networks for Adaptive Control"

Connected through hyperedge HE-4 (induced by source paper "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "A Cognitive Model of Learning to Navigate"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "On the Complexity of Solving Markov Decision Problems"
    - "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
    - "Machine Learning,  Explanation-Based Learning and Reinforcement Learning: A Unified View"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"

Connected through hyperedge HE-5 (induced by source paper "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Exploration and Model Building in Mobile Robot Domains"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Least-Squares Temporal Difference Learning"
    - "An Upper Bound on the Loss from Approximate Optimal-Value Functions"
    - "Monitoring in Embedded Agents"

Connected through hyperedge HE-6 (induced by source paper "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-7 (induced by source paper "Designing Neural Networks for Adaptive Control"):
  with members:
    - "Learning from undiscounted delayed rewards"
    - "Learning Controllers for Industrial Robots"

Connected through hyperedge HE-8 (induced by source paper "Reinforcement Learning Algorithms for Average-Payoff Markovian 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 Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "Auto-exploratory Average Reward Reinforcement Learning"
    - "H-learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"

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