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: "Learning Sparse Perceptrons"
  Abstract: We introduce a new algorithm designed to learn sparse perceptrons over input representations which include high-order features. Our algorithm, which is based on a hypothesis-boosting method, is able to PAC-learn a relatively natural class of target concepts. Moreover, the algorithm appears to work well in practice: on a set of three problem domains, the algorithm produces classifiers that utilize small numbers of features yet exhibit good generalization performance. Perhaps most importantly, our algorithm generates concept descriptions that are easy for humans to understand.

Connected through hyperedge HE-1 (induced by source paper "Learning to Represent Codons: A Challenge Problem for Constructive Induction"):
  with members:
    - "Learning to Predict Reading Frames in E. coli DNA Sequences"
    - "In Defense of C4.5: Notes on Learning One-Level Decision Trees"
    - "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"

Connected through hyperedge HE-2 (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"
    - "On Learning Visual Concepts and DNF Formulae"
    - "On the Sample Complexity of Noise-Tolerant Learning"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"

Connected through hyperedge HE-3 (induced by source paper "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "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"

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