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: "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.

Connected through hyperedge HE-1 (induced by source paper "On Learning Bounded-Width Branching Programs"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-2 (induced by source paper "Learning Probabilistic Automata with Variable Memory Length"):
  with members:
    - "Learning Markov chains with variable memory length from noisy output"
    - "Using and combining predictors that specialize"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"
    - "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"):
  with members:
    - "On Convergence Properties of the EM Algorithm for Gaussian Mixtures"
    - "A Comparison of New and Old Algorithms for A Mixture Estimation Problem"

Connected through hyperedge HE-5 (induced by source paper "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"
    - "On Learning Bounded-Width Branching Programs"
    - "New Evidence Driven State Merging Algorithm"

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