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: "FURTHER FACTS ABOUT INPUT TO STATE STABILIZATION "Further facts about input to state stabilization", IEEE"
  Abstract: Report SYCON-88-15 ABSTRACT Previous results about input to state stabilizability are shown to hold even for systems which are not linear in controls, provided that a more general type of feedback be allowed. Applications to certain stabilization problems and coprime factorizations, as well as comparisons to other results on input to state stability, are also briefly discussed.

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"
    - "Global Stabilization of Linear Discrete-Time Systems with Bounded Feedback"
    - "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 "A Characterization of Integral Input to State Stability"):
  with members:
    - "A Smooth Converse Lyapunov Theorem for Robust Stability"
    - "New Characterizations of Input to State Stability"

Connected through hyperedge HE-3 (induced by source paper "FEEDBACK STABILIZATION OF NONLINEAR SYSTEMS"):
  with members:
    - "FEEDBACK STABILIZATION USING TWO-HIDDEN-LAYER NETS"
    - ""UNIVERSAL" CONSTRUCTION OF ARTSTEIN'S THEOREM ON NONLINEAR STABILIZATION 1"

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