human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "EVOLVING SENSORS IN ENVIRONMENTS OF CONTROLLED COMPLEXITY"):
  Abstract: 1 . Sensors represent a crucial link between the evolutionary forces shaping a species' relationship with its environment, and the individual's cognitive abilities to behave and learn. We report on experiments using a new class of "latent energy environments" (LEE) models to define environments of carefully controlled complexity which allow us to state bounds for random and optimal behaviors that are independent of strategies for achieving the behaviors. Using LEE's analytic basis for defining environments, we then use neural networks (NNets) to model individuals and a steady - state genetic algorithm to model an evolutionary process shaping the NNets, in particular their sensors. Our experiments consider two types of "contact" and "ambient" sensors, and variants where the NNets are not allowed to learn, learn via error correction from internal prediction, and via reinforcement learning. We find that predictive learning, even when using a larger repertoire of the more sophisticated ambient sensors, provides no advantage over NNets unable to learn. However, reinforcement learning using a small number of crude contact sensors does provide a significant advantage. Our analysis of these results points to a tradeoff between the genetic "robustness" of sensors and their informativeness to a learning system.

Includes members:
  Member "Environmental Effects on Minimal Behaviors in the Minimat World":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS")
  Member "Landscapes, Learning Costs and Genetic Assimilation.":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The Evolutionary Cost of Learning")
  Member "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution.":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Evolutionary Cost of Learning")
  Member "Generalization and scaling in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Modeling the Evolution of Motivation")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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