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 DFA from Simple Examples"
  Abstract: We present a framework for learning DFA from simple examples. We show that efficient PAC learning of DFA is possible if the class of distributions is restricted to simple distributions where a teacher might choose examples based on the knowledge of the target concept. This answers an open research question posed in Pitt's seminal paper: Are DFA's PAC-identifiable if examples are drawn from the uniform distribution, or some other known simple distribution? Our approach uses the RPNI algorithm for learning DFA from labeled examples. In particular, we describe an efficient learning algorithm for exact learning of the target DFA with high probability when a bound on the number of states (N ) of the target DFA is known in advance. When N is not known, we show how this algorithm can be used for efficient PAC learning of DFAs.

Connected through hyperedge HE-1 (induced by source paper "Planning and Learning in an Adversarial Robotic Game"):
  with members:
    - "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"
    - "TD Models: Modeling the World at a Mixture of Time Scales"

Connected through hyperedge HE-2 (induced by source paper "Query, PACS and simple-PAC Learning"):
  with members:
    (none)

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"
    - "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"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

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