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: "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
  Abstract: We propose an active learning method with hidden-unit reduction, which is devised specially for multilayer perceptrons (MLP). First, we review our active learning method, and point out that many Fisher-information-based methods applied to MLP have a critical problem: the information matrix may be singular. To solve this problem, we derive the singularity condition of an information matrix, and propose an active learning technique that is applicable to

Connected through hyperedge HE-1 (induced by source paper "Information-based objective functions for active data selection"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "A Practical Bayesian Framework for Backprop Networks"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Improving Generalization with Active Learning"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-2 (induced by source paper "Neural Network Exploration Using Optimal Experiment Design"):
  with members:
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "Finding Promising Exploration Regions by Weighting Expected Navigation Costs continuous environments, some first-order approximations to"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "Exploration Bonuses and Dual Control"

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