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: "Forward models: Supervised learning with a distal teacher"
  Abstract: Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated with the notion of the "teacher" in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multi-layer networks. *This paper is a revised version of MIT Center for Cognitive Science Occasional Paper #40. We wish to thank Michael Mozer, Andrew Barto, Robert Jacobs, Eric Loeb, and James McClelland for helpful comments on the manuscript. This project was supported in part by BRSG 2 S07 RR07047-23 awarded by the Biomedical Research Support Grant Program, Division of Research Resources, National Institutes of Health, by a grant from ATR Auditory and Visual Perception Research Laboratories, by a grant from Siemens Corporation, by a grant from the Human Frontier Science Program, and by grant N00014-90-J-1942 awarded by the Office of Naval Research.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Action Selection Learning Methods"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Cognitive Model of Learning to Navigate"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"

Connected through hyperedge HE-2 (induced by source paper "A Cognitive Model of Learning to Navigate"):
  with members:
    - "Planning and Acting in Partially Observable Stochastic Domains"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"
    - "Reinforcement Learning with Soft State Aggregation"
    - "A Comparison of Action Selection Learning Methods"

Connected through hyperedge HE-3 (induced by source paper "Computational Models of Sensorimotor Integration  Computational Maps and Motor Control."):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"

Connected through hyperedge HE-4 (induced by source paper "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "Refining PID Controllers using Neural Networks"

Connected through hyperedge HE-5 (induced by source paper "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "In  Active Learning with Statistical Models"
    - "Efficient Locally Weighted Polynomial Regression Predictions"
    - "Reinforcement Learning in the Multi-Robot Domain"
    - "Locally Weighted Learning for Control"
    - "Using Local Trajectory Optimizers To Speed Up Global Optimization In Dynamic Programming"

Connected through hyperedge HE-6 (induced by source paper "Acquiring the mapping from meaning to sounds"):
  with members:
    - "Regularities in a Random Mapping from Orthography to Semantics"

Connected through hyperedge HE-7 (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,"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

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