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: "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.

Connected through hyperedge HE-1 (induced by source paper "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"):
  with members:
    - "Adaptive Noise Injection for Input Variables Relevance Determination"
    - "A comparison of some error estimates for neural network models  Summary"

Connected through hyperedge HE-2 (induced by source paper "A proposal for variable selection in the Cox model"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "Least Absolute Shrinkage is Equivalent to Quadratic Penalization"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Adaptive Noise Injection for Input Variables Relevance Determination"

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