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: "Worst-Case Identification of Nonlinear Fading Memory Systems"
  Abstract: In this paper, the problem of asymptotic identification for a class of fading memory systems in the presence of bounded noise is studied. For any experiment, the worst-case error is characterized in terms of the diameter of the worst-case uncertainty set. Optimal inputs that minimize the radius of uncertainty are studied and characterized. Finally, a convergent algorithm that does not require knowledge of the noise upper bound is furnished. The algorithm is based on interpolating data with spline functions, which are shown to be well suited for identification in the presence of bounded noise; more so than other basis functions such as polynomials. The methods as well as the results are quite general and are applicable to a larger variety of settings.

Connected through hyperedge HE-1 (induced by source paper "Robust Convergence of Two-Stage Nonlinear Algorithms for Identification in H 1"):
  with members:
    - "Identification in H 1 with Nonuniformly Spaced Frequency Response Measurements"
    - "OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES"

Connected through hyperedge HE-2 (induced by source paper "OPTIMAL ASYMPTOTIC IDENTIFICATION UNDER BOUNDED DISTURBANCES"):
  with members:
    - "Robust Convergence of Two-Stage Nonlinear Algorithms for Identification in H 1"
    - "Identification in H 1 with Nonuniformly Spaced Frequency Response Measurements"

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