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: "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
  Abstract: This paper presents U-Tree, a reinforcement learning algorithm that uses selective attention and short-term memory to simultaneously address the intertwined problems of large perceptual state spaces and hidden state. By combining the advantages of work in instance-based (or memory-based) learning and work with robust statistical tests for separating noise from task structure, the method learns quickly, creates only task-relevant state distinctions, and handles noise well. U-Tree uses a tree-structured representation, and is related to work on Prediction Suffix Trees [ Ron et al., 1994 ] , Parti-game [ Moore, 1993 ] , G-algorithm [ Chap-man and Kaelbling, 1991 ] , and Variable Resolution Dynamic Programming [ Moore, 1991 ] . It builds on Utile Suffix Memory [ McCallum, 1995c ] , which only used short-term memory, not selective perception. The algorithm is demonstrated solving a highway driving task in which the agent weaves around slower and faster traffic. The agent uses active perception with simulated eye movements. The environment has hidden state, time pressure, stochasticity, over 21,000 world states and over 2,500 percepts. From this environment and sensory system, the agent uses a utile distinction test to build a tree that represents depth-three memory where necessary, and has just 143 internal statesfar fewer than the 2500 3 states that would have resulted from a fixed-sized history-window ap proach.

Connected through hyperedge HE-1 (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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "Learning Probabilistic Automata with Variable Memory Length"):
  with members:
    - "Learning Markov chains with variable memory length from noisy output"
    - "Using and combining predictors that specialize"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
    - "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-3 (induced by source paper "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Learning Probabilistic Automata with Variable Memory Length"

Connected through hyperedge HE-4 (induced by source paper "Reinforcement Learning: A Survey"):
  with members:
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Adding Memory to XCS"

Connected through hyperedge HE-5 (induced by source paper "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Reinforcement Learning in the Multi-Robot Domain"

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