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: "Adaptive Noise Injection for Input Variables Relevance Determination"
  Abstract: In this paper we consider the application of training with noise in multi-layer perceptron to input variables relevance determination. Noise injection is modified in order to penalize irrelevant features. The proposed algorithm is attractive as it requires the tuning of a single parameter. This parameter controls the penalization of the inputs together with the complexity of the model. After the presentation of the method, experimental evidences are given on simulated data sets.

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:
    - "A comparison of some error estimates for neural network models  Summary"
    - "Regression shrinkage and selection via the lasso"

Connected through hyperedge HE-2 (induced by source paper "From Data Distributions to Regularization in Invariant Learning"):
  with members:
    - "for Projective Basis Function Networks 2m1 Global Form 2m Local Form With appropriate constant factors,"
    - "Face Recognition: A Hybrid Neural Network Approach"

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"
    - "Regression shrinkage and selection via the lasso"

Connected through hyperedge HE-4 (induced by source paper "Flexible Metric Nearest Neighbor Classiflcation"):
  with members:
    - "An adaptation of Relief for attribute estimation in regression"
    - "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."
    - "Virtual Seens and the Frequently Used Dataset"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"
    - "DART/HYESS Users Guide  recursive covering approach to local learning"
    - "Selection of Relevant Features and Examples in Machine Learning"

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