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: "Neural competitive maps for reactive and adaptive navigation"
  Abstract: We have recently introduced a neural network for reactive obstacle avoidance based on a model of classical and operant conditioning. In this article we describe the success of this model when implemented on two real autonomous robots. Our results show the promise of self-organizing neural networks in the domain of intelligent robotics.

Connected through hyperedge HE-1 (induced by source paper "An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability,"):
  with members:
    - "Robot Shaping: Developing Situated Agents through 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: Neural_Networks