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: "OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES"
  Abstract: This paper investigates the intrinsic limitation of worst-case identification of LTI systems using data corrupted by bounded disturbances, when the unknown plant is known to belong to a given model set. This is done by analyzing the optimal worst-case asymptotic error achievable by performing experiments using any bounded inputs and estimating the plant using any identification algorithm. First, it is shown that under some topological conditions on the model set, there is an identification algorithm which is asymptotically optimal for any input. Characterization of the optimal asymptotic error as a function of the inputs is also obtained. These results hold for any error metric and disturbance norm. Second, these general results are applied to three specific identification problems: identification of stable systems in the ` 1 norm, identification of stable rational systems in the H 1 norm, and identification of unstable rational systems in the gap metric. For each of these problems, the general characterization of optimal asymptotic error is used to find near-optimal inputs to minimize the error.

Connected through hyperedge HE-1 (induced by source paper "Robust Convergence of Two-Stage Nonlinear Algorithms for Identification in H 1"):
  with members:
    - "Worst-Case Identification of Nonlinear Fading Memory Systems"
    - "Identification in H 1 with Nonuniformly Spaced Frequency Response Measurements"

Connected through hyperedge HE-2 (induced by source paper "Worst-Case Identification of Nonlinear Fading Memory Systems"):
  with members:
    - "Robust Convergence of Two-Stage Nonlinear Algorithms for Identification in H 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