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: "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"
  Abstract: In this paper we describe our study of applying knowledge-based neural networks to the problem of diagnosing faults in local telephone loops. Currently, NYNEX uses an expert system called MAX to aid human experts in diagnosing these faults; however, having an effective learning algorithm in place of MAX would allow easy portability between different maintenance centers, and easy updating when the phone equipment changes. We find that (i) machine learning algorithms have better accuracy than MAX, (ii) neural networks perform better than decision trees, (iii) neural network ensembles perform better than standard neural networks, (iv) knowledge-based neural networks perform better than standard neural networks, and (v) an ensemble of knowledge-based neural networks performs the best.

Connected through hyperedge HE-1 (induced by source paper "Learning from Bad Data"):
  with members:
    - "Actively Searching for an Effective Neural-Network Ensemble"

Connected through hyperedge HE-2 (induced by source paper "Extracting Tree-Structured Representations of Trained Networks"):
  with members:
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"):
  with members:
    - "Constructing Conjunctions using Systematic Search on Decision Trees"
    - "Constructing Nominal Xof-N Attributes"
    - "Extending Theory Refinement to M-of-N Rules"
    - "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Parity: The Problem that Won't Go Away"
    - "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"

Connected through hyperedge HE-4 (induced by source paper "Generating Accurate and Diverse Members of a Neural-Network Ensemble"):
  with members:
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "An Empirical Evaluation of Bagging and Boosting"
    - "A New Metric-Based Approach to Model Selection"

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