human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"):
  Abstract: We study the problem of efficiently learning to play a game optimally against an unknown adversary chosen from a computationally bounded class. We both contribute to the line of research on playing games against finite automata, and expand the scope of this research by considering new classes of adversaries. We introduce the natural notions of games against recent history adversaries (whose current action is determined by some simple boolean formula on the recent history of play), and games against statistical adversaries (whose current action is determined by some simple function of the statistics of the entire history of play). In both cases we give efficient algorithms for learning to play penny-matching and a more difficult game called contract . We also give the most powerful positive result to date for learning to play against finite automata, an efficient algorithm for learning to play any game against any finite automata with probabilistic actions and low cover time.

Includes members:
  Member "A Competitive Approach to Game Learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "New Methods for Competitive Coevolution")
  Member "Planning and Learning in an Adversarial Robotic Game":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Learning DFA from Simple Examples")
  Member "Exactly Learning Automata with Small Cover Time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Power of a Pebble: Exploring and Mapping Directed Graphs")
  Member "The Power of a Pebble: Exploring and Mapping Directed Graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Exactly Learning Automata with Small Cover Time")
  Member "Optimality and Domination in Repeated Games with Bounded Players":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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