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: "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"
  Abstract: The absence of powerful control structures and processes that synchronize, coordinate, switch between, choose among, regulate, direct, modulate interactions between, and combine distinct yet interdependent modules of large connectionist networks (CN) is probably one of the most important reasons why such networks have not yet succeeded at handling difficult tasks (e.g. complex object recognition and description, complex problem-solving, planning). In this paper we examine how CN built from large numbers of relatively simple neuron-like units can be given the ability to handle problems that in typical multi-computer networks and artificial intelligence programs along with all other types of programs are always handled using extremely elaborate and precisely worked out central control (coordination, synchronization, switching, etc.). We point out the several mechanisms for central control of this un-brain-like sort that CN already have built into them albeit in hidden, often overlooked, ways. We examine the kinds of control mechanisms found in computers, programs, fetal development, cellular function and the immune system, evolution, social organizations, and especially brains, that might be of use in CN. Particularly intriguing suggestions are found in the pacemakers, oscillators, and other local sources of the brain's complex partial synchronies; the diffuse, global effects of slow electrical waves and neurohormones; the developmental program that guides fetal development; communication and coordination within and among living cells; the working of the immune system; the evolutionary processes that operate on large populations of organisms; and the great variety of partially competing partially cooperating controls found in small groups, organizations, and larger societies. All these systems are rich in control but typically control that emerges from complex interactions of many local and diffuse sources. We explore how several different kinds of plausible control mechanisms might be incorporated into CN, and assess their potential benefits with respect to their cost.

Connected through hyperedge HE-1 (induced by source paper "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"):
  with members:
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"

Connected through hyperedge HE-2 (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"
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"

Connected through hyperedge HE-3 (induced by source paper "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"):
  with members:
    - "Properties of Genetic Representations of Neural Architectures"
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-4 (induced by source paper "Pruning Strategies for the MTiling Constructive Learning Algorithm"):
  with members:
    - "Generative Learning Structures and Processes for Generalized Connectionist Networks"

Connected through hyperedge HE-5 (induced by source paper "Experiments with the Cascade-Correlation Algorithm"):
  with members:
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Faster Learning in Multi-Layer Networks by Handling"

Connected through hyperedge HE-6 (induced by source paper "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"):
  with members:
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Experiments with the Cascade-Correlation Algorithm"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"

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