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: "Comparison of Bayesian and Neural Net Unsupervised Classification Techniques"
  Abstract: Unsupervised classification is the classification of data into a number of classes in such a way that data in each class are all similar to each other. In the past there have been few if any studies done to compare the performance of different unsupervised classification techniques. In this paper we review Bayesian and neural net approaches to unsupervised classification and present results of experiments that we did to compare Autoclass, a Bayesian classification system, and ART2, a neural net classification algorithm.

Connected through hyperedge HE-1 (induced by source paper "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"):
  with members:
    - "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"
    - "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"
    - "Adaptive Parameter Pruning in Neural Networks"
    - "Connection Pruning with Static and Adaptive Pruning Schedules"
    - "Averaging and Data Snooping"
    - "Statistical Evaluation of Neural Network Experiments: Minimum Requirements and Current Practice"

Connected through hyperedge HE-2 (induced by source paper "Cholinergic suppression of transmission may allow combined associative memory function and self-organization in the neocortex."):
  with members:
    - "Multiassociative Memory"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Neural Network Applicability: Classifying the Problem Space"
    - "Competitive Anti-Hebbian Learning of Invariants"
    - "Multiple Network Systems (Minos) Modules: Task Division and Module Discrimination 1"
    - "Living in a partially structured environment: How to bypass the limitations of classical reinforcement techniques"

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