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: "Exactly Learning Automata with Small Cover Time"
  Abstract: We present algorithms for exactly learning unknown environments that can be described by deterministic finite automata. The learner performs a walk on the target automaton, where at each step it observes the output of the state it is at, and chooses a labeled edge to traverse to the next state. We assume that the learner has no means of a reset, and we also assume that the learner does not have access to a teacher that answers equivalence queries and gives the learner counterexamples to its hypotheses. We present two algorithms, one assumes that the outputs observed by the learner are always correct and the other assumes that the outputs might be erroneous. The running times of both algorithms are polynomial in the cover time of the underlying graph of the target automaton.

Connected through hyperedge HE-1 (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"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-2 (induced by source paper "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"):
  with members:
    - "A Competitive Approach to Game Learning"
    - "Planning and Learning in an Adversarial Robotic Game"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"
    - "Optimality and Domination in Repeated Games with Bounded Players"

Connected through hyperedge HE-3 (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"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"
    - "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