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: "Learning from the Environment by Experimentation: The Need for Few and Informative Examples"
  Abstract: An intelligent system must be able to adapt and learn to correct and update its model of the environment incrementally and deliberately. In complex environments that have many parameters and where interactions have a cost, sampling the possible range of states to test the results of action executions is not a practical approach. We present a practical approach based on continuous and selective interaction with the environment that pinpoints the type of fault in the domain knowledge that causes any unexpected behavior of the environment, and resorts to experimentation when additional information is needed to correct the system's knowledge.

Connected through hyperedge HE-1 (induced by source paper "Discovery as Autonomous Learning from the Environment"):
  with members:
    - "Bayesian Probability Theory A General Method for Machine Learning"
    - "Learning Concepts from Sensor Data of a Mobile Robot"
    - "Learning Finite Automata Using Local Distinguishing Experiments"

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