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 Probabilistic Automata with Variable Memory Length"
  Abstract: We propose and analyze a distribution learning algorithm for variable memory length Markov processes. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Finite Suffix Automata. The learning algorithm is motivated by real applications in man-machine interaction such as handwriting and speech recognition. Conventionally used fixed memory Markov and hidden Markov models have either severe practical or theoretical drawbacks. Though general hardness results are known for learning distributions generated by sources with similar structure, we prove that our algorithm can indeed efficiently learn distributions generated by our more restricted sources. 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 small with high confidence in polynomial time and sample complexity. We demonstrate the applicability of our algorithm by learning the structure of natural English text and using our hy pothesis for the correction of corrupted text.

Connected through hyperedge HE-1 (induced by source paper "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"):
  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"
    - "On Pruning and Averaging Decision Trees"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"
    - "Adaptive Mixtures of Probabilistic Transducers"
    - "Using and combining predictors that specialize"
    - "A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization"

Connected through hyperedge HE-2 (induced by source paper "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"
    - "On Learning Bounded-Width Branching Programs"

Connected through hyperedge HE-3 (induced by source paper "Learning Markov chains with variable memory length from noisy output"):
  with members:
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"

Connected through hyperedge HE-4 (induced by source paper "Using and combining predictors that specialize"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Context-sensitive learning methods for text categorization"
    - "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree"

Connected through hyperedge HE-5 (induced by source paper "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"

Connected through hyperedge HE-6 (induced by source paper "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks"):
  with members:
    - "Reinforcement Learning: A Survey"
    - "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces"
    - "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation."
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

Connected through hyperedge HE-7 (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"
    - "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"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

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