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 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.

Includes members:
  Member "Learning Markov chains with variable memory length from noisy output":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,")
  Member "Using and combining predictors that specialize":
    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 "Machine Learning 27(1):51-68, 1997. Predicting nearly as well as the best pruning of a decision tree":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "On Pruning and Averaging Decision Trees")
  Member "Category: Control, Navigation and Planning. Key words: Reinforcement learning, Exploration, Hidden state. Prefer oral presentation.":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Learning to Act using Real-Time Dynamic Programming")
  Member "Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces")
  Member "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "On the Learnability and Usage of Acyclic Probabilistic Finite Automata")
  Member "On the Learnability and Usage of Acyclic Probabilistic Finite Automata":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "On Learning Bounded-Width Branching Programs")

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