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: "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
  Abstract: Technical Report 1123, Computer Sciences Department, University of Wisconsin - Madison, Nov. 1992 ABSTRACT This article describes an approach to combining symbolic and connectionist approaches to machine learning. A three-stage framework is presented and the research of several groups is reviewed with respect to this framework. The first stage involves the insertion of symbolic knowledge into neural networks, the second addresses the refinement of this prior knowledge in its neural representation, while the third concerns the extraction of the refined symbolic knowledge. Experimental results and open research issues are discussed. A shorter version of this paper will appear in Machine Learning.

Connected through hyperedge HE-1 (induced by source paper "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"

Connected through hyperedge HE-2 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Constructing Nominal Xof-N Attributes"
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Draft Symbolic Representation of Neural Networks"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-3 (induced by source paper "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Strategy Learning with Multilayer Connectionist Representations 1"
    - "Robot Shaping: Developing Situated Agents through Learning"

Connected through hyperedge HE-4 (induced by source paper "Forward models: Supervised learning with a distal teacher"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "A Cognitive Model of Learning to Navigate"
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "References elements that can solve difficult learning control problems. on Simulation of Adaptive Behavior, pages"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Comparison of Action Selection Learning Methods"
    - "Computational Models of Sensorimotor Integration  Computational Maps and Motor Control."

Connected through hyperedge HE-5 (induced by source paper "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "Revising Bayesian Network Parameters Using Backpropagation"
    - "Extending Theory Refinement to M-of-N Rules"
    - "Bias-Driven Revision of Logical Domain Theories"
    - "Comparing Methods for Refining Certainty-Factor Rule-Bases"
    - "Modifying Network Architectures for Certainty-Factor Rule-Base Revision"

Connected through hyperedge HE-6 (induced by source paper "Refining PID Controllers using Neural Networks"):
  with members:
    - "The Neural Network House: An Overview"

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