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: "Flexible Metric Nearest Neighbor Classiflcation"
  Abstract: The K-nearest-neighbor decision rule assigns an object of unknown class to the plurality class among the K labeled "training" objects that are closest to it. Closeness is usually deflned in terms of a metric distance on the Euclidean space with the input measurement variables as axes. The metric chosen to deflne this distance can strongly efiect performance. An optimal choice depends on the problem at hand as characterized by the respective class distributions on the input measurement space, and within a given problem, on the location of the unknown object in that space. In this paper new types of K-nearest-neighbor procedures are described that estimate the local relevance of each input variable, or their linear combinations, for each individual point to be classifled. This information is then used to separately customize the metric used to deflne distance from that object in flnding its nearest neighbors. These procedures are a hybrid between regular K-nearest-neighbor methods and treestructured recursive partitioning techniques popular in statistics and machine learning.

Connected through hyperedge HE-1 (induced by source paper "Selection of Relevant Features and Examples in Machine Learning"):
  with members:
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology"

Connected through hyperedge HE-2 (induced by source paper "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"):
  with members:
    - "The out-of-bootstrap method for model averaging and selection"
    - "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
    - "Combining estimates in regression and classification"
    - "A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection"
    - "Bias, variance and prediction error for classification rules"
    - "The covariance inflation criterion for adaptive model selection"
    - "Unbiased Assessment of Learning Algorithms"
    - "Estimating the Accuracy of Learned Concepts"

Connected through hyperedge HE-3 (induced by source paper "Parzen. On estimation of a probability density function and mode. Annual Mathematical Statistics, 33:1065-1076, 1962."):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Learning networks for face analysis and synthesis"
    - "Learning in neural networks with Bayesian prototypes"
    - "MDL Learning of Probabilistic Neural Networks for Discrete Problem Domains"
    - "A Fast Non-Parametric Density Estimation Algorithm"
    - "Efficient Non-parametric Estimation of Probability Density Functions"

Connected through hyperedge HE-4 (induced by source paper "Adaptive Noise Injection for Input Variables Relevance Determination"):
  with members:
    - "From Data Distributions to Regularization in Invariant Learning"
    - "Least Absolute Shrinkage is Equivalent to Quadratic Penalization"
    - "Penalisation multiple adaptative un nouvel algorithme de regression, la penalisation multiple adapta-tive. Cet algorithme represente"

Connected through hyperedge HE-5 (induced by source paper "An adaptation of Relief for attribute estimation in regression"):
  with members:
    - "Employing Linear Regression in Regression Tree Leaves"
    - "Estimating Attributes: Analysis and Extensions of RELIEF"
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Overcoming the myopia of inductive learning algorithms with RELIEFF"

Connected through hyperedge HE-6 (induced by source paper "Virtual Seens and the Frequently Used Dataset"):
  with members:
    (none)

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