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: "Generalization and scaling in reinforcement learning"
  Abstract: In associative reinforcement learning, an environment generates input vectors, a learning system generates possible output vectors, and a reinforcement function computes feedback signals from the input-output pairs. The task is to discover and remember input-output pairs that generate rewards. Especially difficult cases occur when rewards are rare, since the expected time for any algorithm can grow exponentially with the size of the problem. Nonetheless, if a reinforcement function possesses regularities, and a learning algorithm exploits them, learning time can be reduced below that of non-generalizing algorithms. This paper describes a neural network algorithm called complementary reinforcement back-propagation (CRBP), and reports simulation results on problems designed to offer differing opportunities for generalization.

Connected through hyperedge HE-1 (induced by source paper "Modeling the Evolution of Motivation"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Auto-teaching: networks that develop their own teaching input"
    - "An Analysis of the MAX Problem in Genetic Programming hold only in some cases, in"

Connected through hyperedge HE-2 (induced by source paper "Emergent Control and Planning in an Autonomous Vehicle"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Adaptation in constant utility non-stationary environments"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "learning easier tasks. More work is necessary in order to determine more precisely the relationship"
    - "Improving the Performance of Evolutionary Optimization by Dynamically Scaling the Evaluation Function"

Connected through hyperedge HE-4 (induced by source paper "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"):
  with members:
    - "Environmental Effects on Minimal Behaviors in the Minimat World"
    - "Landscapes, Learning Costs and Genetic Assimilation."
    - "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."

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