human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution"):
  Abstract: It is often difficult to predict the optimal neural network size for a particular application. Constructive or destructive methods that add or subtract neurons, layers, connections, etc. might offer a solution to this problem. We prove that one method, Recurrent Cascade Correlation, due to its topology, has fundamental limitations in representation and thus in its learning capabilities. It cannot represent with monotone (i.e. sigmoid) and hard-threshold activation functions certain finite state automata. We give a "preliminary" approach on how to get around these limitations by devising a simple constructive training method that adds neurons during training while still preserving the powerful fully-recurrent structure. We illustrate this approach by simulations which learn many examples of regular grammars that the

Includes members:
  Member "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Induction of Multiscale Temporal Structure")
  Member "First-Order vs. Second-Order Single Layer Recurrent Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks")
  Member "Even with Arbitrary Transfer Functions, RCC Cannot Compute Certain FSA":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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