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 "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"):
  Abstract: This paper describes new and efficient algorithms for learning deterministic finite automata. Our approach is primarily distinguished by two features: (1) the adoption of an average-case setting to model the "typical" labeling of a finite automaton, while retaining a worst-case model for the underlying graph of the automaton, along with (2) a learning model in which the learner is not provided with the means to experiment with the machine, but rather must learn solely by observing the automaton's output behavior on a random input sequence. The main contribution of this paper is in presenting the first efficient algorithms for learning non-trivial classes of automata in an entirely passive learning model. We adopt an on-line learning model in which the learner is asked to predict the output of the next state, given the next symbol of the random input sequence; the goal of the learner is to make as few prediction mistakes as possible. Assuming the learner has a means of resetting the target machine to a fixed start state, we first present an efficient algorithm that makes an expected polynomial number of mistakes in this model. Next, we show how this first algorithm can be used as a subroutine by a second algorithm that also makes a polynomial number of mistakes even in the absence of a reset. Along the way, we prove a number of combinatorial results for randomly labeled automata. We also show that the labeling of the states and the bits of the input sequence need not be truly random, but merely semi-random. Finally, we discuss an extension of our results to a model in which automata are used to represent distributions over binary strings.

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 "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Exactly Learning Automata with Small Cover Time")
  Member "The Power of a Pebble: Exploring and Mapping Directed Graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Exactly Learning Automata with Small Cover Time")
  Member "On Learning Bounded-Width Branching Programs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "On the Learnability and Usage of Acyclic Probabilistic Finite Automata")
  Member "New Evidence Driven State Merging Algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction")
  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