human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "A Practical Bayesian Framework for Backprop Networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection")
  Member "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators")
  Member "The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Predictions with Confidence Intervals (Local Error Bars)")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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