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: "Improving Generalization with Active Learning"
  Abstract: Active learning differs from passive "learning from examples" in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful that learning from examples alone, giving better generalization for a fixed number of training examples. In this paper, we consider the problem of learning a binary concept in the absence of noise (Valiant 1984). We describe a formalism for active concept learning called selective sampling, and show how it may be approximately implemented by a neural network. In selective sampling, a learner receives distribution information from the environment and queries an oracle on parts of the domain it considers "useful." We test our implementation, called an SG-network, on three domains, and observe significant improvement in generalization.

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"
    - "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 "Active Learning with Committees for Text Categorization"):
  with members:
    - "Query by Committee"
    - "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"
    - "Applying Winnow to Context-Sensitive Spelling Correction"

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