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: "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"
  Abstract: In learning problems where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source of unneeded weights comes from the inclusion of input variables that provide little information about the output variables. We propose a method for identifying and eliminating these input variables. The method first determines the relationship between input and output variables using nonparametric density estimation and then measures the relevance of input variables using the information theoretic concept of mutual information. We present results from our method on a simple toy problem and a nonlinear time series.

Connected through hyperedge HE-1 (induced by source paper "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING"):
  with members:
    - "Evaluating Neural Network Predictors by Bootstrapping"
    - "Predictions with Confidence Intervals (Local Error Bars)"
    - "Predicting Conditional Probability Distributions: A Connectionist Approach"

Connected through hyperedge HE-2 (induced by source paper "A Practical Bayesian Framework for Backprop Networks"):
  with members:
    - "Density Networks and their Application to Protein Modelling"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Ensemble Learning and Evidence Maximization"
    - "Probabilistic Networks: New Models and New Methods"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

Connected through hyperedge HE-3 (induced by source paper "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Using Errors to Create Piecewise Learnable Partitions"
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
    - "Feature Subset Selection as Search with Probabilistic Estimates"

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: Probabilistic_Methods