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 "Regression shrinkage and selection via the lasso"):
  Abstract: We propose a new method for estimation in linear models. The "lasso" minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly zero and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.

Includes members:
  Member "Least Absolute Shrinkage is Equivalent to Quadratic Penalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A comparison of some error estimates for neural network models  Summary")
  Member "A proposal for variable selection in the Cox model":
    also connected through other hyperedges:
      (none)

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