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: "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.

Connected through hyperedge HE-1 (induced by source paper "Planning and Learning in an Adversarial Robotic Game"):
  with members:
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Learning DFA from Simple Examples"

Connected through hyperedge HE-2 (induced by source paper "Exactly Learning Automata with Small Cover Time"):
  with members:
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
    - "The Power of a Pebble: Exploring and Mapping Directed Graphs"

Connected through hyperedge HE-3 (induced by source paper "The Power of a Pebble: Exploring and Mapping Directed Graphs"):
  with members:
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"
    - "Exactly Learning Automata with Small Cover Time"
    - "Efficient Learning of Typical Finite Automata from Random Walks (Extended Abstract)"

Connected through hyperedge HE-4 (induced by source paper "Optimality and Domination in Repeated Games with Bounded Players"):
  with members:
    (none)

Connected through hyperedge HE-5 (induced by source paper "A Competitive Approach to Game Learning"):
  with members:
    - "Markov games as a framework for multi-agent reinforcement learning"
    - "New Methods for Competitive Coevolution"

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