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: "An unsupervised neural network for low-level control of a wheeled mobile robot: noise resistance, stability,"
  Abstract: We have recently introduced a neural network mobile robot controller (NETMORC) that autonomously learns the forward and inverse odometry of a differential drive robot through an unsupervised learning-by-doing cycle. After an initial learning phase, the controller can move the robot to an arbitrary stationary or moving target while compensating for noise and other forms of disturbance, such as wheel slippage or changes in the robot's plant. In addition, the forward odometric map allows the robot to reach targets in the absence of sensory feedback. The controller is also able to adapt in response to long-term changes in the robot's plant, such as a change in the radius of the wheels. In this article we review the NETMORC architecture and describe its simplified algorithmic implementation, we present new, quantitative results on NETMORC's performance and adaptability under noise-free and noisy conditions, we compare NETMORC's performance on a trajectory-following task with the performance of an alternative controller, and we describe preliminary results on the hardware implementation of NETMORC with the mobile robot ROBUTER.

Connected through hyperedge HE-1 (induced by source paper "Neural competitive maps for reactive and adaptive navigation"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Robot Shaping: Developing Situated Agents through Learning"):
  with members:
    - "Learning to Act using Real-Time Dynamic Programming"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Learning to Race: Experiments with a Simulated Race Car"
    - "Adapting Control Strategies for Situated Autonomous Agents"
    - "Genetics-based Machine Learning and Behaviour Based Robotics: A New Synthesis complexity grows, the learning task"
    - "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"
    - "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL"
    - "The Role of the Trainer in Reinforcement 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: Neural_Networks