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 "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"):
  Abstract: We propose and analyze a distribution learning algorithm for a subclass of Acyclic Probabilistic Finite Automata (APFA). This subclass is characterized by a certain distinguishability property of the automata's states. Though hardness results are known for learning distributions generated by general APFAs, we prove that our algorithm can efficiently learn distributions generated by the subclass of APFAs we consider. In particular, we show that the KL-divergence between the distribution generated by the target source and the distribution generated by our hypothesis can be made arbitrarily small with high confidence in polynomial time. We present two applications of our algorithm. In the first, we show how to model cursively written letters. The resulting models are part of a complete cursive handwriting recognition system. In the second application we demonstrate how APFAs can be used to build multiple-pronunciation models for spoken words. We evaluate the APFA based pronunciation models on labeled speech data. The good performance (in terms of the log-likelihood obtained on test data) achieved by the APFAs and the little time needed for learning suggests that the learning algorithm of APFAs might be a powerful alternative to commonly used probabilistic models.

Includes members:
  Member "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A General Lower Bound on the Number of Examples Needed for Learning")
  Member "Learning Probabilistic Automata with Variable Memory Length":
    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 "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Comparison of New and Old Algorithms for A Mixture Estimation Problem")
  Member "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "On Learning Bounded-Width Branching Programs")
  Member "On Learning Bounded-Width Branching Programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata")

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