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: "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
  Abstract: This paper specifies the main features of Brain-like, Neuronal, and Connectionist models; argues for the need for, and usefulness of, appropriate successively larger brain-like structures; and examines parallel-hierarchical Recognition Cone models of perception from this perspective, as examples of such structures. The anatomy, physiology, behavior, and development of the visual system are briefly summarized to motivate the architecture of brain-structured networks for perceptual recognition. Results are presented from simulations of carefully pre-designed Recognition Cone structures that perceive objects (e.g., houses) in digitized photographs. A framework for perceptual learning is introduced, including mechanisms for generation-discovery (feedback-guided growth of new links and nodes, subject to brain-like constraints (e.g., local receptive fields, global convergence-divergence). The information processing transforms discovered through generation are fine-tuned by feedback-guided reweight-ing of links. Some preliminary results are presented of brain-structured networks that learn to recognize simple objects (e.g., letters of the alphabet, cups, apples, bananas) through feedback-guided generation and reweighting. These show large improvements over networks that either lack brain-like structure or/and learn by reweighting of links alone.

Connected through hyperedge HE-1 (induced by source paper "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"):
  with members:
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"

Connected through hyperedge HE-2 (induced by source paper "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"):
  with members:
    - "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"

Connected through hyperedge HE-3 (induced by source paper "Experiments with the Cascade-Correlation Algorithm"):
  with members:
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"
    - "Faster Learning in Multi-Layer Networks by Handling"

Connected through hyperedge HE-4 (induced by source paper "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"):
  with members:
    - "Experiments with the Cascade-Correlation Algorithm"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"
    - "Pruning Strategies for the MTiling Constructive Learning Algorithm"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

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