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: "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.

Connected through hyperedge HE-1 (induced by source paper "Learning from Examples, Agent Teams and the Concept of Reflection"):
  with members:
    - "The Pandemonium System of Reflective Agents"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"

Connected through hyperedge HE-2 (induced by source paper "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"):
  with members:
    - "Back Propagation is Sensitive to Initial Conditions"

Connected through hyperedge HE-3 (induced by source paper "Rejection of Incorrect Answers from a Neural Net Classifier"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"):
  with members:
    - "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."
    - "The Pandemonium System of Reflective Agents"
    - "Learning from Examples, Agent Teams and the Concept of Reflection"

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