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: "Heterogeneous Uncertainty Sampling for Supervised Learning"
  Abstract: Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.

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"
    - "Improving Generalization with Active Learning"
    - "REINFORCEMENT DRIVEN INFORMATION ACQUISITION IN NON-DETERMINISTIC ENVIRONMENTS"
    - "Advances in Neural Information Processing Systems 8 Active Learning in Multilayer Perceptrons"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."

Connected through hyperedge HE-2 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning Trees and Rules with Set-valued Features"
    - "Mistake-Driven Learning in Text Categorization"
    - "Using and combining predictors that specialize"
    - "Transferring and Retraining Learned Information Filters"

Connected through hyperedge HE-3 (induced by source paper "Learning Trees and Rules with Set-valued Features"):
  with members:
    - "Context-sensitive learning methods for text categorization"
    - "Transferring and Retraining Learned Information Filters"
    - "Inverse entailment and Progol"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

Connected through hyperedge HE-4 (induced by source paper "Query by Committee"):
  with members:
    - "Sifting informative examples from a random source."
    - "Active Learning with Committees for Text Categorization"
    - "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "On-Site Learning"

Connected through hyperedge HE-5 (induced by source paper "More Efficient Windowing"):
  with members:
    - "Incremental Reduced Error Pruning"

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