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 Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
  Abstract: We use simulated soccer to study multiagent learning. Each team's players (agents) share action set and policy but may behave differently due to position-dependent inputs. All agents making up a team are rewarded or punished collectively in case of goals. We conduct simulations with varying team sizes, and compare two learning algorithms: TD-Q learning with linear neural networks (TD-Q) and Probabilistic Incremental Program Evolution (PIPE). TD-Q is based on evaluation functions (EFs) mapping input/action pairs to expected reward, while PIPE searches policy space directly. PIPE uses adaptive "probabilistic prototype trees" to synthesize programs that calculate action probabilities from current inputs. Our results show that TD-Q encounters several difficulties in learning appropriate shared EFs. PIPE, however, does not depend on EFs and can find good policies faster and more reliably. This suggests that in multiagent learning scenarios direct search through policy space can offer advantages over EF-based approaches.

Connected through hyperedge HE-1 (induced by source paper "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"):
  with members:
    - "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"
    - "A Computer Scientist's View of Life, the Universe, and Everything"
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"

Connected through hyperedge HE-2 (induced by source paper "In  Improving Elevator Performance Using Reinforcement Learning"):
  with members:
    - "Submitted to NIPS96, Section: Applications. Preference: Oral presentation Reinforcement Learning for Dynamic Channel Allocation in"
    - "NEUROCONTROL BY REINFORCEMENT LEARNING"
    - "Spurious Solutions to the Bellman Equation"
    - "Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning"
    - "Reinforcement Learning Methods for Continuous-Time Markov Decision Problems"

Connected through hyperedge HE-3 (induced by source paper "Markov games as a framework for multi-agent reinforcement learning"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "A Competitive Approach to Game Learning"
    - "database"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent 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