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: "Selective sampling using the Query by Committee algorithm Running title: Selective sampling using Query by Committee"
  Abstract: We analyze the "query by committee" algorithm, a method for filtering informative queries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gain with positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of perceptrons. Keywords: selective sampling, query learning, Bayesian Learning, experimental design fl Yoav Freund, Room 2B-428, AT&T Laboratories, 700 Mountain Ave., Murray Hill, NJ, 07974. Telephone:908-582-3164.

Connected through hyperedge HE-1 (induced by source paper "Active Learning with Committees for Text Categorization"):
  with members:
    - "Improving Generalization with Active Learning"
    - "Query by Committee"
    - "Applying Winnow to Context-Sensitive Spelling Correction"

Connected through hyperedge HE-2 (induced by source paper "Query by Committee"):
  with members:
    - "Sifting informative examples from a random source."
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "Active Learning with Committees for Text Categorization"
    - "An Algorithm for Active Data Collection for Learning Feasibility Study with Neural Networks."
    - "On-Site Learning"

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