human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "General Bounds on Statistical Query Learning and PAC Learning with Noise via Hypothesis Boosting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "25 Learning in Hybrid Noise Environments Using Statistical Queries")
  Member "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree")
  Member "Learning to Represent Codons: A Challenge Problem for Constructive Induction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Learning to Predict Reading Frames in E. coli DNA Sequences")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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