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 "Bayesian Non-linear Modelling for the Prediction Competition"):
  Abstract: The 1993 energy prediction competition involved the prediction of a series of building energy loads from a series of environmental input variables. Non-linear regression using `neural networks' is a popular technique for such modeling tasks. Since it is not obvious how large a time-window of inputs is appropriate, or what preprocessing of inputs is best, this can be viewed as a regression problem in which there are many possible input variables, some of which may actually be irrelevant to the prediction of the output variable. Because a finite data set will show random correlations between the irrelevant inputs and the output, any conventional neural network (even with reg-ularisation or `weight decay') will not set the coefficients for these junk inputs to zero. Thus the irrelevant variables will hurt the model's performance. The Automatic Relevance Determination (ARD) model puts a prior over the regression parameters which embodies the concept of relevance. This is done in a simple and `soft' way by introducing multiple regularisation constants, one associated with each input. Using Bayesian methods, the regularisation constants for junk inputs are automatically inferred to be large, preventing those inputs from causing significant overfitting.

Includes members:
  Member "Probabilistic Networks: New Models and New Methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Interpolation Models with Multiple")
  Member "A Practical Bayesian Framework for Backprop Networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Information-based objective functions for active data selection")
  Member "Interpolation Models with Multiple":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Probabilistic Networks: New Models and New Methods")

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: Neural_Networks