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: "Acquiring the mapping from meaning to sounds"
  Abstract: 1 We thank Steen Ladegaard Knudsen for his assistance in programming, analysis and running of simulations, Scott Baden for his assistance in vectorizing our code for the Cray Y-MP, the Division of Engineering Block Grant for time on the Cray at the San Diego Supercomputer Center, and the members of the PDPNLP and GURU Research Groups at UCSD for helpful comments on earlier versions of this work.

Connected through hyperedge HE-1 (induced by source paper "Regularities in a Random Mapping from Orthography to Semantics"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Forward models: Supervised learning with a distal teacher"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Cognitive Model of Learning to Navigate"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "A Comparison of Action Selection Learning Methods"

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