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: "Learning from Bad Data"
  Abstract: The data describing resolutions to telephone network local loop "troubles," from which we wish to learn rules for dispatching technicians, are notoriously unreliable. Anecdotes abound detailing reasons why a resolution entered by a technician would not be valid, ranging from sympathy to fear to ignorance to negligence to management pressure. In this paper, we describe four different approaches to dealing with the problem of "bad" data in order first to determine whether machine learning has promise in this domain, and then to determine how well machine learning might perform. We then offer evidence that machine learning can help to build a dispatching method that will perform better than the system currently in place.

Connected through hyperedge HE-1 (induced by source paper "Using Neural Networks to Automatically Refine Expert System Knowledge Bases: Experiments in the NYNEX MAX Domain"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"
    - "Extracting Tree-Structured Representations of Trained Networks"

Connected through hyperedge HE-2 (induced by source paper "Actively Searching for an Effective Neural-Network Ensemble"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"
    - "An Empirical Evaluation of Bagging and Boosting"

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