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 "Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency?"):
  Abstract: There are currently several types of constructive, or growth, algorithms available for training a feed-forward neural network. This paper describes and explains the main ones, using a fundamental approach to the multi-layer perceptron problem-solving mechanisms. The claimed convergence properties of the algorithms are verified using just two mapping theorems, which consequently enables all the algorithms to be unified under a basic mechanism. The algorithms are compared and contrasted and the deficiencies of some highlighted. The fundamental reasons for the actual success of these algorithms are extracted, and used to suggest where they might most fruitfully be applied. A suspicion that they are not a panacea for all current neural network difficulties, and that one must somewhere along the line pay for the learning efficiency they promise, is developed into an argument that their generalization abilities will lie on average below that of back-propagation.

Includes members:
  Member "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Back Propagation is Sensitive to Initial Conditions")
  Member "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "The Pandemonium System of Reflective Agents")
  Member "Learning from Examples, Agent Teams and the Concept of Reflection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The Pandemonium System of Reflective Agents")
  Member "Rejection of Incorrect Answers from a Neural Net Classifier":
    also connected through other hyperedges:
      (none)

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