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: "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.

Connected through hyperedge HE-1 (induced by source paper "Landscapes, Learning Costs and Genetic Assimilation."):
  with members:
    - "The Evolutionary Cost of Learning"
    - "Genes, Phenes and the Baldwin Effect: Learning and Evolution in a Simulated Population"

Connected through hyperedge HE-2 (induced by source paper "Environmental Effects on Minimal Behaviors in the Minimat World"):
  with members:
    - "Issues in Evolutionary Robotics"
    - "Complex Environments to Complex Behaviors  FROM COMPLEX ENVIRONMENTS TO COMPLEX BEHAVIORS"
    - "Generalist and Specialist Behavior Due to Individual Energy Extracting Abilities."

Connected through hyperedge HE-3 (induced by source paper "Generalization and scaling in reinforcement learning"):
  with members:
    - "Modeling the Evolution of Motivation"
    - "Adaptation in constant utility non-stationary environments"
    - "Emergent Control and Planning in an Autonomous Vehicle"

Connected through hyperedge HE-4 (induced by source paper "Guiding or Hiding: Explorations into the Effects of Learning on the Rate of Evolution."):
  with members:
    - "The Evolutionary Cost of 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