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: "Adding Memory to XCS"
  Abstract: We add internal memory to the XCS classifier system. We then test XCS with internal memory, named XCSM, in non-Markovian environments with two and four aliasing states. Experimental results show that XCSM can easily converge to optimal solutions in simple environments; moreover, XCSM's performance is very stable with respect to the size of the internal memory involved in learning. However, the results we present evidence that in more complex non-Markovian environments, XCSM may fail to evolve an optimal solution. Our results suggest that this happens because, the exploration strategies currently employed with XCS, are not adequate to guarantee the convergence to an optimal policy with XCSM, in complex non-Markovian environments.

Connected through hyperedge HE-1 (induced by source paper "Reinforcement Learning: A Survey"):
  with members:
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"

Connected through hyperedge HE-2 (induced by source paper "A Study of the Generalization Capabilities of XCS"):
  with members:
    - "Model of the Environment to Avoid Local Learning"
    - "GENETIC AND NON GENETIC OPERATORS IN ALECSYS"
    - "Environments with Classifier Systems (Experiments on Adding Memory to XCS)"

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