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: "Boosting a weak learning algorithm by majority To be published in Information and Computation"
  Abstract: We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas presented by Schapire in his paper "The strength of weak learnability", and represents an improvement over his results. The analysis of our algorithm provides general upper bounds on the resources required for learning in Valiant's polynomial PAC learning framework, which are the best general upper bounds known today. We show that the number of hypotheses that are combined by our algorithm is the smallest number possible. Other outcomes of our analysis are results regarding the representational power of threshold circuits, the relation between learnability and compression, and a method for parallelizing PAC learning algorithms. We provide extensions of our algorithms to cases in which the concepts are not binary and to the case where the accuracy of the learning algorithm depends on the distribution of the instances.

Connected through hyperedge HE-1 (induced by source paper "Efficient Distribution-free Learning of Probabilistic Concepts"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning in the Presence of Malicious Errors"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Prediction, Learning, Uniform Convergence, and Scale-sensitive Dimensions"
    - "Toward Efficient Agnostic Learning"
    - "Learning Switching Concepts"

Connected through hyperedge HE-2 (induced by source paper "A Quantum Computational Learning Algorithm"):
  with members:
    - "Weakly Learning DNF and Characterizing Statistical Query Learning Using Fourier Analysis"

Connected through hyperedge HE-3 (induced by source paper "On the Sample Complexity of Weakly Learning"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Teaching a Smarter Learner"
    - "Exact Identification of Read-once Formulas Using Fixed Points of Amplification Functions"

Connected through hyperedge HE-4 (induced by source paper "Learning From a Population of Hypotheses"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"

Connected through hyperedge HE-5 (induced by source paper "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting"):
  with members:
    - "25 Learning in Hybrid Noise Environments Using Statistical Queries"
    - "Learning Sparse Perceptrons"
    - "On Learning Visual Concepts and DNF Formulae"
    - "On the Sample Complexity of Noise-Tolerant Learning"

Connected through hyperedge HE-6 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Learning Sparse Perceptrons"
    - "An Efficient Boosting Algorithm for Combining Preferences"
    - "BOOSTING AND NAIVE BAYESIAN LEARNING"
    - "Actively Searching for an Effective Neural-Network Ensemble"
    - "Learning to Order Things"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Context-sensitive learning methods for text categorization"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-7 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"):
  with members:
    - "Learning DFA from Simple Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Sequential PAC Learning"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "On the Sample Complexity of Weakly Learning"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-8 (induced by source paper "Game Theory, On-line Prediction and Boosting"):
  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"
    - "Predicting a binary sequence almost as well as the optimal biased coin"

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