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: "Automated Highway System"
  Abstract: ALVINN (Autonomous Land Vehicle in a Neural Net) is a Backpropagation trained neural network which is capable of autonomously steering a vehicle in road and highway environments. Although ALVINN is fairly robust, one of the problems with it has been the time it takes to train. As the vehicle is capable of on-line learning, the driver has to drive the car for about 2 minutes before the network is capable of autonomous operation. One reason for this is the use of Backprop. In this report, we describe the original ALVINN system, and then look at three alternative training methods - Quickprop, Cascade Correlation, and Cascade 2. We then run a series of trials using Quickprop, Cascade Correlation and Cascade2, and compare them to a BackProp baseline. Finally, a hidden unit analysis is performed to determine what the network is learning. Applying Advanced Learning Algorithms to ALVINN

Connected through hyperedge HE-1 (induced by source paper "MANIAC: A Next Generation Neurally Based Autonomous Road Follower"):
  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