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: "Learning Where To Go without Knowing Where That Is: The Acquisition of a Non-reactive Mobot"
  Abstract: In the path-imitation task, one agent traces out a path through a second agent's sensory field. The second agent then has to reproduce that path exactly, i.e. move through the sequence of locations visited by the first agent. This is a non-trivial behaviour whose acquisition might be expected to involve special-purpose (i.e., strongly biased) learning machinery. However, the present paper shows this is not the case. The behaviour can be acquired using a fairly primitive learning regime provided that the agent's environment can be made to pass through a specific sequence of dynamic states.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Learning with the Soft-Means Algorithm"):
  with members:
    (none)

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: Genetic_Algorithms