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: "On-Site Learning"
  Abstract: A model for on-site learning is presented. The system learns by querying "hard" patterns while classifying "easy" ones. This model is related to query-based filtering methods, but takes into account that in addition to labelling, filtering through the data has a cost. A few simple policies are introduced and analyzed for a simple problem (1D high low game). In addition the Query-by-Committee algorithm (Seung et. al) is suggested as a good approximator of the model space for real-world domains. Results using this algorithm on a synthesized problem and a real-world OCR task using both a backpropagation network and a nearest neighbor classifier show that an on-site learner can perform as well as a classifier trained off-site, while achieving significant cost reduction.

Connected through hyperedge HE-1 (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"
    - "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."

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