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: "Parametric regression 1.1 Learning problem model f bw b w in turn is an estimator"
  Abstract: Let us present briefly the learning problem we will address in this chapter and the following. The ultimate goal is the modelling of a mapping f : x 7! y from multidimensional input x to output y. The output can be multi-dimensional, but we will mostly address situations where it is a one dimensional real value. Furthermore, we should take into account the fact that we scarcely ever observe the actual true mapping y = f (x). This is due to perturbations such as e.g. observational noise. We will rather have a joint probability p (x; y). We expect this probability to be peaked for values of x and y corresponding to the mapping. We focus on automatic learning by example. A set D = of data sampled from the joint distribution p (x; y) = p (yjx) p (x) is collected. With the help of this set, we try to identify a model of the data, parameterised by a set of 1.2 Learning and optimisation The fit of the model to the system in a given point x is measured using a criterion representing the distance from the model prediction b y to the system, e (y; f w (x)). This is the local risk . The performance of the model is measured by the expected This quantity represents the ability to yield good performance for all the possible situations (i.e. (x; y) pairs) and is thus called generalisation error . The optimal set 1 parameters w: f w : x 7! b y.

Connected through hyperedge HE-1 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-2 (induced by source paper "Bias, variance and prediction error for classification rules"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "An Efficient Method To Estimate Bagging's Generalization Error"
    - "82 Lag-space estimation in time-series modelling keep track of cases where the estimation of P"

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