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: "Information-based objective functions for active data selection"
  Abstract: Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed which measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.

Connected through hyperedge HE-1 (induced by source paper "A Practical Bayesian Framework for Backprop Networks"):
  with members:
    - "Density Networks and their Application to Protein Modelling"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "Using Neural Networks for Descriptive Statistical Analysis of Educational Data"
    - "Ensemble Learning and Evidence Maximization"
    - "Probabilistic Networks: New Models and New Methods"
    - "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights"
    - "Bayesian Non-linear Modelling for the Prediction Competition"

Connected through hyperedge HE-2 (induced by source paper "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"):
  with members:
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-3 (induced by source paper "In  Active Learning with Statistical Models"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "In:  A Mixture Model System for Medical and Machine Diagnosis"
    - "Control Systems Magazine, 14, 1, pp.57-71. Robot Juggling: An Implementation of Memory-based Learning"
    - "Q2: Memory-based active learning for optimizing noisy continuous functions field expands beyond prediction and function"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-4 (induced by source paper "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Query by Committee"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-5 (induced by source paper "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"):
  with members:
    - "In  Active Learning with Statistical Models"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-6 (induced by source paper "In:  A Mixture Model System for Medical and Machine Diagnosis"):
  with members:
    - "Supervised learning from incomplete data via an EM approach"
    - "Using Temporal-Difference Reinforcement Learning to Improve Decision-Theoretic Utilities for Diagnosis"
    - "In  Active Learning with Statistical Models"
    - "Neural Network Exploration Using Optimal Experiment Design"

Connected through hyperedge HE-7 (induced by source paper "Improving Generalization with Active Learning"):
  with members:
    - "Active Learning with Committees for Text Categorization"

Connected through hyperedge HE-8 (induced by source paper "Heterogeneous Uncertainty Sampling for Supervised Learning"):
  with members:
    - "More Efficient Windowing"
    - "Learning Trees and Rules with Set-valued Features"
    - "Context-sensitive learning methods for text categorization"
    - "Query by Committee"

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: Theory