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 Controllers for Industrial Robots"
  Abstract: One of the most significant cost factors in robotics applications is the design and development of real-time robot control software. Control theory helps when linear controllers have to be developed, but it doesn't sufficiently support the generation of non-linear controllers, although in many cases (such as in compliance control), nonlinear control is essential for achieving high performance. This paper discusses how Machine Learning has been applied to the design of (non-)linear controllers. Several alternative function approximators, including Multilayer Perceptrons (MLP), Radial Basis Function Networks (RBFNs), and Fuzzy Controllers are analyzed and compared, leading to the definition of two major families: Open Field Function Function Approximators and Locally Receptive Field Function Approximators. It is shown that RBFNs and Fuzzy Controllers bear strong similarities, and that both have a symbolic interpretation. This characteristics allows for applying both symbolic and statistic learning algorithms to synthesize the network layout from a set of examples and, possibly, some background knowledge. Three integrated learning algorithms, two of which are original, are described and evaluated on experimental test cases. The first test case is provided by a robot KUKA IR-361 engaged into the "peg-into-hole" task, whereas the second is represented by a classical prediction task on the Mackey-Glass time series. From the experimental comparison, it appears that both Fuzzy Controllers and RBFNs synthesised from examples are excellent approximators, and that, in practice, they can be even more accurate than MLPs.

Connected through hyperedge HE-1 (induced by source paper "FONN: Combining First Order Logic with Connectionist Learning"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"

Connected through hyperedge HE-2 (induced by source paper "Designing Neural Networks for Adaptive Control"):
  with members:
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Learning from undiscounted delayed rewards"

Connected through hyperedge HE-3 (induced by source paper "GAL: Networks that grow when they learn and shrink when they forget"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Comparison of Kernel Estimators, Perceptrons, and Radial-Basis Functions for OCR and Speech Classification"

Connected through hyperedge HE-4 (induced by source paper "Learning Concepts from Sensor Data of a Mobile Robot"):
  with members:
    - "Discovery as Autonomous Learning from the Environment"

Connected through hyperedge HE-5 (induced by source paper "GROWING RADIAL BASIS FUNCTION NETWORKS"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "Learning networks for face analysis and synthesis"
    - "GA-RBF: A Self-Optimising RBF Network"
    - "Growing Cell Structures A Self-organizing Network for Unsupervised and Supervised Learning"

Connected through hyperedge HE-6 (induced by source paper "GA-RBF: A Self-Optimising RBF Network"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Evolving Fuzzy Prototypes for Efficient Data Clustering"

Connected through hyperedge HE-7 (induced by source paper "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "USING MARKER-BASED GENETIC ENCODING OF NEURAL NETWORKS TO EVOLVE FINITE-STATE BEHAVIOUR"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Modular Q-Learning Architecture for Manipulator Task Decomposition `Data storage in the cerebellar model ar"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"
    - "A Neural Network Pole Balancer that Learns and Operates on a Real Robot in Real Time"
    - "Q-Learning with Hidden-Unit Restarting"

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