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: "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.

Connected through hyperedge HE-1 (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"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions"
    - "On Learning Bounded-Width Branching Programs"

Connected through hyperedge HE-2 (induced by source paper "On Learning Bounded-Width Branching Programs"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-3 (induced by source paper "New Evidence Driven State Merging Algorithm"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"

Connected through hyperedge HE-4 (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"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-5 (induced by source paper "The Power of a Pebble: Exploring and Mapping Directed Graphs"):
  with members:
    - "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
    - "Exactly Learning Automata with Small Cover Time"

Connected through hyperedge HE-6 (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-7 (induced by source paper "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Learning From a Population of Hypotheses"
    - "Exactly Learning Automata with Small Cover Time"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"

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