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: "MANIAC: A Next Generation Neurally Based Autonomous Road Follower"
  Abstract: The use of artificial neural networks in the domain of autonomous vehicle navigation has produced promising results. ALVINN [Pomerleau, 1991] has shown that a neural system can drive a vehicle reliably and safely on many different types of roads, ranging from paved paths to interstate highways. Even with these impressive results, several areas within the neural paradigm for autonomous road following still need to be addressed. These include transparent navigation between roads of different type, simultaneous use of different sensors, and generalization to road types which the neural system has never seen. The system presented here addresses these issue with a modular neural architecture which uses pre-trained ALVINN networks and a connectionist superstructure to robustly drive on many dif ferent types of roads.

Connected through hyperedge HE-1 (induced by source paper "Automated Highway System"):
  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: Neural_Networks