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: "Classifying Seismic Signals by Integrating Ensembles of Neural Networks"
  Abstract: This paper proposes a classification scheme based on integration of multiple Ensembles of ANNs. It is demonstrated on a classification problem, in which seismic signals of Natural Earthquakes must be distinguished from seismic signals of Artificial Explosions. A Redundant Classification Environment consists of several Ensembles of Neural Networks is created and trained on Bootstrap Sample Sets, using various data representations and architectures. The ANNs within the Ensembles are aggregated (as in Bagging) while the Ensembles are integrated non-linearly, in a signal adaptive manner, using a posterior confidence measure based on the agreement (variance) within the Ensembles. The proposed Integrated Classification Machine achieved 92.1% correct classifications on the seismic test data. Cross Validation evaluations and comparisons indicate that such integration of a collection of ANN's Ensembles is a robust way for handling high dimensional problems with a complex non-stationary signal space as in the current Seismic Classification problem.

Connected through hyperedge HE-1 (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"
    - "Flexible Metric Nearest Neighbor Classiflcation"
    - "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-2 (induced by source paper "Combining estimates in regression and classification"):
  with members:
    - "A Method of Combining Multiple Probabilistic Classifiers through Soft Competition on Different Feature Sets"
    - "Stacked Density Estimation"
    - "Rule-based Machine Learning Methods for Functional Prediction"
    - "Error-Correcting Output Coding Corrects Bias and Variance"
    - "Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule"

Connected through hyperedge HE-3 (induced by source paper "Hierarchical Mixtures of Experts and the EM Algorithm"):
  with members:
    - "Validation of Average Error Rate Over Classifiers"
    - "A HIERARCHICAL COMMUNITY OF EXPERTS"
    - "Comparison of Neural and Statistical Classifiers| Theory and Practice"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Supervised learning from incomplete data via an EM approach"
    - "A Statistical Approach to Decision Tree Modeling"
    - "On learning hierarchical classifications"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"

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