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: "A Competitive Approach to Game Learning"
  Abstract: Machine learning of game strategies has often depended on competitive methods that continually develop new strategies capable of defeating previous ones. We use a very inclusive definition of game and consider a framework within which a competitive algorithm makes repeated use of a strategy learning component that can learn strategies which defeat a given set of opponents. We describe game learning in terms of sets H and X of first and second player strategies, and connect the model with more familiar models of concept learning. We show the importance of the ideas of teaching set [20] and specification number [19] k in this new context. The performance of several competitive algorithms is investigated, using both worst-case and randomized strategy learning algorithms. Our central result (Theorem 4) is a competitive algorithm that solves games in a total number of strategies polynomial in lg(jHj), lg(jX j), and k. Its use is demonstrated, including an application in concept learning with a new kind of counterexample oracle. We conclude with a complexity analysis of game learning, and list a number of new questions arising from this work.

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

Connected through hyperedge HE-2 (induced by source paper "New Methods for Competitive Coevolution"):
  with members:
    - "Tackling the Boolean Even N Parity Problem with Genetic Programming and Limited-Error Fitness standard GP"
    - "Every Niching Method has its Niche: Fitness Sharing and Implicit Sharing Compared"
    - "17 Massively Parallel Genetic Programming"
    - "Automatic Modularization by Speciation"
    - "Small Populations over Many Generations can beat Large Populations over Few Generations in Genetic Programming"
    - "Dynamics of Co-evolutionary Learning"

Connected through hyperedge HE-3 (induced by source paper "Markov games as a framework for multi-agent reinforcement learning"):
  with members:
    - "Generalized Markov Decision Processes: Dynamic-programming and Reinforcement-learning Algorithms"
    - "Learning Team Strategies With Multiple Policy-Sharing Agents: A Soccer Case Study"
    - "database"
    - "Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm"
    - "Multi-Agent Reinforcement Learning: Independent vs. Cooperative Agents"
    - "Learning Conventions in Multiagent Stochastic Domains using Likelihood Estimates"
    - "Reinforcement Learning with Imitation in Heterogeneous Multi-Agent Systems"
    - "Using Communication to Reduce Locality in Distributed Multi-Agent Learning"

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