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: "Least Absolute Shrinkage is Equivalent to Quadratic Penalization"
  Abstract: Adaptive ridge is a special form of ridge regression, balancing the quadratic penalization on each parameter of the model. This paper shows the equivalence between adaptive ridge and lasso (least absolute shrinkage and selection operator). This equivalence states that both procedures produce the same estimate. Least absolute shrinkage can thus be viewed as a particular quadratic penalization. From this observation, we derive an EM algorithm to compute the lasso solution. We finally present a series of applications of this type of algorithm in regres sion problems: kernel regression, additive modeling and neural net training.

Connected through hyperedge HE-1 (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-2 (induced by source paper "Adaptive Noise Injection for Input Variables Relevance Determination"):
  with members:
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "From Data Distributions to Regularization in Invariant Learning"
    - "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"

Connected through hyperedge HE-3 (induced by source paper "Regression shrinkage and selection via the lasso"):
  with members:
    - "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"
    - "A proposal for variable selection in the Cox model"

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