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 Concepts from Sensor Data of a Mobile Robot"
  Abstract: Machine learning can be a most valuable tool for improving the flexibility and efficiency of robot applications. Many approaches to applying machine learning to robotics are known. Some approaches enhance the robot's high-level processing, the planning capabilities. Other approaches enhance the low-level processing, the control of basic actions. In contrast, the approach presented in this paper uses machine learning for enhancing the link between the low-level representations of sensing and action and the high-level representation of planning. The aim is to facilitate the communication between the robot and the human user. A hierarchy of concepts is learned from route records of a mobile robot. Perception and action are combined at every level, i.e., the concepts are perceptually anchored. The relational learning algorithm grdt has been developed which completely searches in a hypothesis space, that is restricted by rule schemata, which the user defines in terms of grammars.

Connected through hyperedge HE-1 (induced by source paper "Learning Controllers for Industrial Robots"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "GAL: Networks that grow when they learn and shrink when they forget"
    - "Designing Neural Networks for Adaptive Control"
    - "GA-RBF: A Self-Optimising RBF Network"
    - "FONN: Combining First Order Logic with Connectionist Learning"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"

Connected through hyperedge HE-2 (induced by source paper "Discovery as Autonomous Learning from the Environment"):
  with members:
    - "Bayesian Probability Theory A General Method for Machine Learning"
    - "Learning Finite Automata Using Local Distinguishing Experiments"
    - "Learning from the Environment by Experimentation: The Need for Few and Informative Examples"

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