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: "Planning and Learning in an Adversarial Robotic Game"
  Abstract: 1 This paper demonstrates the tandem use of a finite automata learning algorithm and a utility planner for an adversarial robotic domain. For many applications, robot agents need to predict the movement of objects in the environment and plan to avoid them. When the robot has no reasoning model of the object, machine learning techniques can be used to generate one. In our project, we learn a DFA model of an adversarial robot and use the automaton to predict the next move of the adversary. The robot agent plans a path to avoid the adversary at the predicted location while fulfilling the goal requirements.

Connected through hyperedge HE-1 (induced by source paper "Learning DFA from Simple Examples"):
  with members:
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "Query, PACS and simple-PAC Learning"

Connected through hyperedge HE-2 (induced by source paper "TD Models: Modeling the World at a Mixture of Time Scales"):
  with members:
    - "Planning with Closed-Loop Macro Actions"
    - "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
    - "Multi-time Models for Temporally Abstract Planning"
    - "Multi-time Models for Temporally"
    - "Multi-Time Models for Reinforcement Learning"

Connected through hyperedge HE-3 (induced by source paper "Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries"):
  with members:
    - "A Competitive Approach to Game Learning"
    - "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"

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