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 Characterization of Integral Input to State Stability"
  Abstract: Just as input to state stability (iss) generalizes the idea of finite gains with respect to supremum norms, the new notion of integral input to state stability (iiss) generalizes the concept of finite gain when using an integral norm on inputs. In this paper, we obtain a necessary and sufficient characterization of the iiss property, expressed in terms of dissipation inequalities.

Connected through hyperedge HE-1 (induced by source paper "A Smooth Converse Lyapunov Theorem for Robust Stability"):
  with members:
    - "New Characterizations of Input to State Stability"

Connected through hyperedge HE-2 (induced by source paper "New Characterizations of Input to State Stability"):
  with members:
    - "A Smooth Converse Lyapunov Theorem for Robust 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-3 (induced by source paper "FURTHER FACTS ABOUT INPUT TO STATE STABILIZATION "Further facts about input to state stabilization", IEEE"):
  with members:
    - "New Characterizations of Input to State Stability"
    - "FEEDBACK STABILIZATION OF NONLINEAR SYSTEMS"

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