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: "Perceptual Development and Learning: From Behavioral, Neurophysiological, and Morphological Evidence To Computational Models"
  Abstract: An intelligent system has to be capable of adapting to a constantly changing environment. It therefore, ought to be capable of learning from its perceptual interactions with its surroundings. This requires a certain amount of plasticity in its structure. Any attempt to model the perceptual capabilities of a living system or, for that matter, to construct a synthetic system of comparable abilities, must therefore, account for such plasticity through a variety of developmental and learning mechanisms. This paper examines some results from neuroanatomical, morphological, as well as behavioral studies of the development of visual perception; integrates them into a computational framework; and suggests several interesting experiments with computational models that can yield insights into the development of visual perception. In order to understand the development of information processing structures in the brain, one needs knowledge of changes it undergoes from birth to maturity in the context of a normal environment. However, knowledge of its development in aberrant settings is also extremely useful, because it reveals the extent to which the development is a function of environmental experience (as opposed to genetically determined pre-wiring). Accordingly, we consider development of the visual system under both normal and restricted rearing conditions. The role of experience in the early development of the sensory systems in general, and the visual system in particular, has been widely studied through a variety of experiments involving carefully controlled manipulation of the environment presented to an animal. Extensive reviews of such results can be found in (Mitchell, 1984; Movshon, 1981; Hirsch, 1986; Boothe, 1986; Singer, 1986). Some examples of manipulation of visual experience are total pattern deprivation (e.g., dark rearing), selective deprivation of a certain class of patterns (e.g., vertical lines), monocular deprivation in animals with binocular vision, etc. Extensive studies involving behavioral deficits resulting from total visual pattern deprivation indicate that the deficits arise primarily as a result of impairment of visual information processing in the brain. The results of these experiments suggest specific developmental or learning mechanisms that may be operating at various stages of development, and at different levels in the system. We will discuss some of these hhhhhhhhhhhhhhh This is a working draft. All comments, especially constructive criticism and suggestions for improvement, will be appreciated. I am indebted to Prof. James Dannemiller for introducing me to some of the literature in infant development; to Prof. Leonard Uhr for his helpful comments on an initial draft of the paper; and to numerous researchers whose experimental work has provided the basis for the model outlined in this paper. This research was partially supported by grants from the National Science Foundation and the University of Wisconsin Graduate School.

Connected through hyperedge HE-1 (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"
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"

Connected through hyperedge HE-2 (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"
    - "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"
    - "Experiments with the Cascade-Correlation Algorithm"

Connected through hyperedge HE-3 (induced by source paper "Coordination and Control Structures and Processes: Possibilities for Connectionist Networks (CN)"):
  with members:
    - "BRAINSTRUCTURED CONNECTIONIST NETWORKS THAT PERCEIVE AND LEARN"
    - "Experiments with the Cascade-Correlation Algorithm"
    - "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