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: "2-D Pole Balancing with Recurrent Evolutionary Networks"
  Abstract: The success of evolutionary methods on standard control learning tasks has created a need for new benchmarks. The classic pole balancing problem is no longer difficult enough to serve as a viable yardstick for measuring the learning efficiency of these systems. In this paper we present a more difficult version to the classic problem where the cart and pole can move in a plane. We demonstrate a neuroevolution system (Enforced Sub-Populations, or ESP) that can solve this difficult problem without velocity information.

Connected through hyperedge HE-1 (induced by source paper "Evolving Obstacle Avoidance Behavior in a Robot Arm"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Hierarchical Evolution of Neural Networks"
    - "Issues in Evolutionary Robotics"
    - "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"

Connected through hyperedge HE-2 (induced by source paper "Incremental Evolution of Complex General Behavior"):
  with members:
    - "Real-time Interactive Neuro-evolution"

Connected through hyperedge HE-3 (induced by source paper "Machine Learning,  Efficient Reinforcement Learning through Symbiotic Evolution"):
  with members:
    - "Real-time Interactive Neuro-evolution"
    - "Evolving Obstacle Avoidance Behavior in a Robot Arm"
    - "Evolutionary Neural Networks for Value Ordering in Constraint Satisfaction Problems"
    - "EVOLVING NEURAL NETWORKS WITH COLLABORATIVE SPECIES"
    - "A Coevolutionary Approach to Learning Sequential Decision Rules"

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