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: "A Smooth Converse Lyapunov Theorem for Robust Stability"
  Abstract: This paper presents a Converse Lyapunov Function Theorem motivated by robust control analysis and design. Our result is based upon, but generalizes, various aspects of well-known classical theorems. In a unified and natural manner, it (1) allows arbitrary bounded time-varying parameters in the system description, (2) deals with global asymptotic stability, (3) results in smooth (infinitely differentiable) Lyapunov functions, and (4) applies to stability with respect to not necessarily compact invariant sets. 1. Introduction. This work is motivated by problems of robust nonlinear stabilization. One of our main

Connected through hyperedge HE-1 (induced by source paper "New Characterizations of Input to State Stability"):
  with members:
    - "A Characterization of Integral Input to State Stability"
    - "FURTHER FACTS ABOUT INPUT TO STATE STABILIZATION "Further facts about input to state stabilization", IEEE"
    - "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:
    - "New Characterizations of Input to State Stability"
    - "FURTHER FACTS ABOUT INPUT TO STATE STABILIZATION "Further facts about input to state stabilization", IEEE"

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