human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Combining Connectionist and Symbolic Learning to Refine Certainty-Factor Rule Bases":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Extending Theory Refinement to M-of-N Rules")
  Member "Symbolic and Subsymbolic Learning for Vision: Some Possibilities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley.")
  Member "Forward models: Supervised learning with a distal teacher":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "A Comparison of Action Selection Learning Methods")
  Member "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "Effects of Different Types of New Attribute on Constructive Induction")
  Member "Coordinating Reactive Behaviors  keywords: reactive systems, planning and learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "Strategy Learning with Multilayer Connectionist Representations 1")
  Member "REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Strategy Learning with Multilayer Connectionist Representations 1")
  Member "Refining PID Controllers using Neural Networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "The Neural Network House: An Overview")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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