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: "Two Methods for Hierarchy Learning in Reinforcement Environments"
  Abstract: This paper describes two methods for hierarchically organizing temporal behaviors. The first is more intuitive: grouping together common sequences of events into single units so that they may be treated as individual behaviors. This system immediately encounters problems, however, because the units are binary, meaning the behaviors must execute completely or not at all, and this hinders the construction of good training algorithms. The system also runs into difficulty when more than one unit is (or should be) active at the same time. The second system is a hierarchy of transition values. This hierarchy dynamically modifies the values that specify the degree to which one unit should follow another. These values are continuous, allowing the use of gradient descent during learning. Furthermore, many units are active at the same time as part of the system's normal functionings.

Connected through hyperedge HE-1 (induced by source paper "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "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)"

Connected through hyperedge HE-2 (induced by source paper "ENVIRONMENT-INDEPENDENT REINFORCEMENT ACCELERATION  difference between time and space is that you can't reuse time."):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "ON LEARNING HOW TO LEARN LEARNING STRATEGIES"

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