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: "Even with Arbitrary Transfer Functions, RCC Cannot Compute Certain FSA"
  Abstract: Category: algorithms and architectures | recurrent networks. No part of this paper has been submitted elsewhere. Preference: poster. Abstract Existing proofs demonstrating the computational limitations of the Recurrent Cascade Correlation (RCC) Network (Fahlman, 1991) explicitly limit their results to units having sigmoidal or hard-threshold transfer functions (Giles et al., 1995; and Kremer, 1996). The proof given here shows that, for any given finite, discrete, deterministic transfer function used by the units of an RCC network, there are finite-state automata (FSA) that the network cannot model, no matter how many units are used. The proof applies equally well to continuous transfer functions with a finite number of fixed-points, such as the sigmoid function.

Connected through hyperedge HE-1 (induced by source paper "Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"

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