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: "Global Stabilization of Linear Discrete-Time Systems with Bounded Feedback"
  Abstract: This paper deals with the problem of global stabilization of linear discrete time systems by means of bounded feedback laws. The main result proved is an analog of one proved for the continuous time case by the authors, and shows that such stabilization is possible if and only if the system is stabilizable with arbitrary controls and the transition matrix has spectral radius less or equal to one. The proof provides in principle an algorithm for the construction of such feedback laws, which can be implemented either as cascades or as parallel connections ("single hidden layer neural networks") of simple saturation functions.

Connected through hyperedge HE-1 (induced by source paper "New Characterizations of Input to State Stability"):
  with members:
    - "A Smooth Converse Lyapunov Theorem for Robust Stability"
    - "A Characterization of Integral Input to State Stability"
    - "FURTHER FACTS ABOUT INPUT TO STATE STABILIZATION "Further facts about input to state stabilization", IEEE"
    - "On Finite Gain Stabilizability of Linear Systems Subject to Input Saturation"
    - "Changing Supply Functions in Input/State Stable Systems"

Connected through hyperedge HE-2 (induced by source paper "j"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "On Finite Gain Stabilizability of Linear Systems Subject to Input Saturation"):
  with members:
    - "New Characterizations of Input to State Stability"
    - "References Linear Controller Design, Limits of Performance, "The parallel projection operators of a nonlinear feedback"

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