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: "Book Review New Kids on the Block way in the field of connectionist modeling. The"
  Abstract: Connectionist Models is a collection of forty papers representing a wide variety of research topics in connectionism. The book is distinguished by a single feature: the papers are almost exclusively contributions of graduate students active in the field. The students were selected by a rigorous review process and participated in a two week long summer school devoted to connectionism 2 . As the ambitious editors state in the foreword: These are bold claims and, if true, the reader is presented with an exciting opportunity to sample the frontiers of connectionism. Their words imply two ways to approach the book. The book must be read not just as a random collection of scientific papers, but also as a challenge to evaluate a controversial field. 2 This summer school is actually the third in a series, previous ones being held in 1986 and 1988. The proceedings of the 1988 summer school (which I had the priviledge of participating in) are reviewed by Nigel Goddard in [4]. Continuing the pattern, a fourth school is scheduled to be held in 1993 in Boulder, CO.

Connected through hyperedge HE-1 (induced by source paper "Unsupervised Neural Network Learning Procedures For Feature Extraction and Classification"):
  with members:
    - "LEARNING FACTORIAL CODES BY PREDICTABILITY MINIMIZATION (Neural Computation, 4(6):863-879, 1992)"
    - "VISIT: An Efficient Computational Model of Human Visual Attention"
    - "A Non-linear Information Maximisation Algorithm that Performs Blind Separation."
    - "Local Multivariate Binary Processors"

Connected through hyperedge HE-2 (induced by source paper "Efficient Visual Search: A Connectionist Solution"):
  with members:
    - "VISIT: An Efficient Computational Model of Human Visual Attention"

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