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: "Applying Online Search Techniques to Continuous-State Reinforcement Learning key to the success of the local"
  Abstract: In this paper, we describe methods for efficiently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boost the power of very approximate value functions discovered by traditional reinforcement learning techniques. We examine local searches, where the agent performs a finite-depth lookahead search, and global searches, where the agent performs a search for a trajectory all the way from the current state to a goal state. The key to the success of the global methods lies in using aggressive state-space search techniques such as uniform-cost search and A fl , tamed into a tractable form by exploiting neighborhood relations and trajectory constraints that arise from continuous-space dynamic control.

Connected through hyperedge HE-1 (induced by source paper "Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "Fast Online Q()"
    - "Modeling the Student with Reinforcement Learning"
    - "Learning in Continuous Domains with Delayed Rewards"
    - "Generality versus Size in Genetic Programming"

Connected through hyperedge HE-2 (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"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "On the Complexity of Solving Markov Decision Problems"
    - "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-3 (induced by source paper "Learning to Act using Real-Time Dynamic Programming"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function"
    - "Learning sorting and decision trees with POMDPs"
    - "Markov Decision Processes in Large State Spaces"
    - "Learning Curve Bounds for Markov Decision Processes with Undiscounted Rewards"
    - "The Efficient Learning of Multiple Task Sequences"
    - "Exploration and Model Building in Mobile Robot Domains"

Connected through hyperedge HE-4 (induced by source paper "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time"
    - "Q-Learning with Hidden-Unit Restarting"

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