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: "Hyperplane Dynamics as a Means to Understanding Back-Propagation Learning and Network Plasticity"
  Abstract: The processing performed by a feed-forward neural network is often interpreted through use of decision hyperplanes at each layer. The adaptation process, however, is normally explained using the picture of gradient descent of an error landscape. In this paper the dynamics of the decision hyperplanes is used as the model of the adaptation process. A electro-mechanical analogy is drawn where the dynamics of hyperplanes is determined by interaction forces between hyperplanes and the particles which represent the patterns. Relaxation of the system is determined by increasing hyperplane inertia (mass). This picture is used to clarify the dynamics of learning, and to go some way to explaining learning deadlocks and escaping from certain local minima. Furthermore network plasticity is introduced as a dynamic property of the system, and its reduction as a necessary consequence of information storage. Hyper-plane inertia is used to explain and avoid destructive relearning in trained networks.

Connected through hyperedge HE-1 (induced by source paper "Submitted to Circuits, Systems and Signal Processing Neural Network Constructive Algorithms: Trading Generalization for Learning Efficiency?"):
  with members:
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Learning from Examples, Agent Teams and the Concept of Reflection"
    - "Rejection of Incorrect Answers from a Neural Net Classifier"

Connected through hyperedge HE-2 (induced by source paper "Back Propagation is Sensitive to Initial Conditions"):
  with members:
    - "Multiassociative Memory"
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Scaling-up RAAMs"
    - "Replicability of Neural Computing Experiments"
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Neural Network Implementation in SAS R Software"

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