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: "FONN: Combining First Order Logic with Connectionist Learning"
  Abstract: This paper presents a neural network architecture that can manage structured data and refine knowledge bases expressed in a first order logic language. The presented framework is well suited to classification problems in which concept de scriptions depend upon numerical features of the data. In fact, the main goal of the neural architecture is that of refining the numerical part of the knowledge base, without changing its structure. In particular, we discuss a method to translate a set of classification rules into neural computation units. Here, we focus our attention on the translation method and on algorithms to refine network weights on struc tured data. The classification theory to be refined can be manually handcrafted or automatically acquired by a symbolic relational learning system able to deal with numerical features. As a matter of fact, the primary goal is to bring into a neural network architecture the capability of dealing with structured data of unrestricted size, by allowing to dynamically bind the classification rules to different items occur ring in the input data. An extensive experimentation on a challenging artificial case study shows that the network converges quite fastly and generalizes much better than propositional learners on an equivalent task definition.

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"
    - "GROWING RADIAL BASIS FUNCTION NETWORKS"
    - "Learning Concepts from Sensor Data of a Mobile Robot"

Connected through hyperedge HE-2 (induced by source paper "Learning networks for face analysis and synthesis"):
  with members:
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Learning from incomplete data"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "Regularization Theory and Neural Networks Architectures"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "Efficient Implementation of Gaussian Processes"

Connected through hyperedge HE-3 (induced by source paper "Comparing Methods for Refining Certainty-Factor Rule-Bases"):
  with members:
    - "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"
    - "Bias-Driven Revision of Logical Domain Theories"
    - "On the Informativeness of the DNA Promoter Sequences Domain Theory"

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