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: "Sifting informative examples from a random source."
  Abstract: We discuss two types of algorithms for selecting relevant examples that have been developed in the context of computation learning theory. The examples are selected out of a stream of examples that are generated independently at random. The first two algorithms are the so-called "boosting" algorithms of Schapire [ Schapire, 1990 ] and Fre-und [ Freund, 1990 ] , and the Query-by-Committee algorithm of Seung [ Seung et al., 1992 ] . We describe the algorithms and some of their proven properties, point to some of their commonalities, and suggest some possible future implications.

Connected through hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning"):
  with members:
    - "Learning in the Presence of Malicious Errors"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Rigorous Learning Curve Bounds from Statistical Mechanics"
    - "Sequential PAC Learning"
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Inductive Logic Programming"
    - "Pac Learning, Noise, and Geometry"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-2 (induced by source paper "Boosting a weak learning algorithm by majority To be published in Information and Computation"):
  with members:
    - "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Learning From a Population of Hypotheses"
    - "Game Theory, On-line Prediction and Boosting"
    - "On the Sample Complexity of Weakly Learning"
    - "A Quantum Computational Learning Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Query by Committee"):
  with members:
    - "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."
    - "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