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: "On Learning Bounded-Width Branching Programs"
  Abstract: In this paper, we study PAC-learning algorithms for specialized classes of deterministic finite automata (DFA). In particular, we study branching programs, and we investigate the influence of the width of the branching program on the difficulty of the learning problem. We first present a distribution-free algorithm for learning width-2 branching programs. We also give an algorithm for the proper learning of width-2 branching programs under uniform distribution on labeled samples. We then show that the existence of an efficient algorithm for learning width-3 branching programs would imply the existence of an efficient algorithm for learning DNF, which is not known to be the case. Finally, we show that the existence of an algorithm for learning width-3 branching programs would also yield an algorithm for learning a very restricted version of parity with noise.

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

Connected through hyperedge HE-2 (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-3 (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"
    - "Learning Probabilistic Automata with Variable Memory Length"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"
    - "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