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 "Recurrent Neural Networks: Some Systems-Theoretic Aspects"):
  Abstract: This paper provides an exposition of some recent research regarding system-theoretic aspects of continuous-time recurrent (dynamic) neural networks with sigmoidal activation functions. The class of systems is introduced and discussed, and a result is cited regarding their universal approximation properties. Known characterizations of controllability, ob-servability, and parameter identifiability are reviewed, as well as a result on minimality. Facts regarding the computational power of recurrent nets are also mentioned. fl Supported in part by US Air Force Grant AFOSR-94-0293

Includes members:
  Member "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS")
  Member "NETWORKS, FUNCTION DETERMINES FORM":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical")
  Member "OBSERVABILITY IN RECURRENT NEURAL NETWORKS":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "NETWORKS, FUNCTION DETERMINES FORM")
  Member "Complete Controllability of Continuous-Time Recurrent Neural Networks":
    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