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 "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"):
  Abstract: We examine the representational capabilities of first-order and second-order Single Layer Recurrent Neural Networks (SLRNNs) with hard-limiting neurons. We show that a second-order SLRNN is strictly more powerful than a first-order SLRNN. However, if the first-order SLRNN is augmented with output layers of feedforward neurons, it can implement any finite-state recognizer, but only if state-splitting is employed. When a state is split, it is divided into two equivalent states. The judicious use of state-splitting allows for efficient implementation of finite-state recognizers using augmented first-order SLRNNs.

Includes members:
  Member "POWER OF NEURAL NETS":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to")
  Member "Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2")
  Member "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns")

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