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: "Robot Shaping: Developing Situated Agents through Learning"
  Abstract: Learning plays a vital role in the development of situated agents. In this paper, we explore the use of reinforcement learning to "shape" a robot to perform a predefined target behavior. We connect both simulated and real robots to A LECSYS, a parallel implementation of a learning classifier system with an extended genetic algorithm. After classifying different kinds of Animat-like behaviors, we explore the effects on learning of different types of agent's architecture (monolithic, flat and hierarchical) and of training strategies. In particular, hierarchical architecture requires the agent to learn how to coordinate basic learned responses. We show that the best results are achieved when both the agent's architecture and the training strategy match the structure of the behavior pattern to be learned. We report the results of a number of experiments carried out both in simulated and in real environments, and show that the results of simulations carry smoothly to real robots. While most of our experiments deal with simple reactive behavior, in one of them we demonstrate the use of a simple and general memory mechanism. As a whole, our experimental activity demonstrates that classifier systems with genetic algorithms can be practically employed to develop autonomous agents.

Connected through hyperedge HE-1 (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-2 (induced by source paper "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"):
  with members:
    - "On the Relations Between Search and Evolutionary Algorithms"
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "The Role of the Trainer in Reinforcement Learning"
    - "A Study of the Generalization Capabilities of XCS"
    - "ROBO-SHEPHERD: LEARNING COMPLEX ROBOTIC BEHAVIORS"

Connected through hyperedge HE-3 (induced by source paper "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"

Connected through hyperedge HE-4 (induced by source paper "An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability,"):
  with members:
    - "Neural competitive maps for reactive and adaptive navigation"

Connected through hyperedge HE-5 (induced by source paper "Learning to Race: Experiments with a Simulated Race Car"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Strategy Learning with Multilayer Connectionist Representations 1"

Connected through hyperedge HE-6 (induced by source paper "The Role of the Trainer in Reinforcement Learning"):
  with members:
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Learning Sequential Decision Rules Using Simulation Models and Competition"
    - "The Role of the Trainer in Reinforcement Learning"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"
    - "EVOLVING ROBOT BEHAVIORS"
    - "EVOLUTIONARY ALGORITHMS IN ROBOTICS"
    - "An Evolutionary Approach to Learning in Robots"

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