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: "Generative Learning Structures and Processes for Generalized Connectionist Networks"
  Abstract: Massively parallel networks of relatively simple computing elements offer an attractive and versatile framework for exploring a variety of learning structures and processes for intelligent systems. This paper briefly summarizes some popular learning structures and processes used in such networks. It outlines a range of potentially more powerful alternatives for pattern-directed inductive learning in such systems. It motivates and develops a class of new learning algorithms for massively parallel networks of simple computing elements. We call this class of learning processes generative for they offer a set of mechanisms for constructive and adaptive determination of the network architecture the number of processing elements and the connectivity among them as a function of experience. Generative learning algorithms attempt to overcome some of the limitations of some approaches to learning in networks that rely on modification of weights on the links within an otherwise fixed network topology e.g., rather slow learning and the need for an a-priori choice of a network architecture. Several alternative designs as well as a range of control structures and processes which can be used to regulate the form and content of internal representations learned by such networks are examined. Empirical results from the study of some generative learning algorithms are briefly summarized and several extensions and refinements of such algorithms, and directions for future research are outlined.

Connected through hyperedge HE-1 (induced by source paper "Properties of Genetic Representations of Neural Architectures"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Analysis of Neurocontrollers Designed by Simulated Evolution"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"

Connected through hyperedge HE-2 (induced by source paper "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"):
  with members:
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"
    - "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"

Connected through hyperedge HE-3 (induced by source paper "Symbolic and Subsymbolic Learning for Vision: Some Possibilities"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Some Biases for Efficient Learning of Spatial, Temporal, and Spatio-Temporal Patterns"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"

Connected through hyperedge HE-4 (induced by source paper "Faster Learning in Multi-Layer Networks by Handling"):
  with members:
    - "Experiments with the Cascade-Correlation Algorithm"

Connected through hyperedge HE-5 (induced by source paper "Analysis of Decision Boundaries Generated by Constructive Neural Network Learning Algorithms"):
  with members:
    - "Properties of Genetic Representations of Neural Architectures"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"
    - "A Simple Randomized Quantization Algorithm for Neural Network Pattern Classifiers"

Connected through hyperedge HE-6 (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"
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"

Connected through hyperedge HE-7 (induced by source paper "Pruning Strategies for the MTiling Constructive Learning Algorithm"):
  with members:
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"

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