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: "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
  Abstract: We show that two cooperating robots can learn exactly any strongly-connected directed graph with n indistinguishable nodes in expected time polynomial in n. We introduce a new type of homing sequence for two robots, which helps the robots recognize certain previously-seen nodes. We then present an algorithm in which the robots learn the graph and the homing sequence simultaneously by actively wandering through the graph. Unlike most previous learning results using homing sequences, our algorithm does not require a teacher to provide counterexamples. Furthermore, the algorithm can use efficiently any additional information available that distinguishes nodes. We also present an algorithm in which the robots learn by taking random walks. The rate at which a random walk on a graph converges to the stationary distribution is characterized by the conductance of the graph. Our random-walk algorithm learns in expected time polynomial in n and in the inverse of the conductance and is more efficient than the homing-sequence algorithm for high-conductance graphs.

Connected through hyperedge HE-1 (induced by source paper "Exactly Learning Automata with Small Cover Time"):
  with members:
    - "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"

Connected through hyperedge HE-2 (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"
    - "Exactly Learning Automata with Small Cover Time"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-3 (induced by source paper "Learning From a Population of Hypotheses"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"

Connected through hyperedge HE-4 (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 a Pebble: Exploring and Mapping Directed Graphs"
    - "On Learning Bounded-Width Branching Programs"
    - "New Evidence Driven State Merging Algorithm"
    - "On the Learnability and Usage of Acyclic Probabilistic Finite Automata"

Connected through hyperedge HE-5 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"

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