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: "Geometry in Learning"
  Abstract: One of the fundamental problems in learning is identifying members of two different classes. For example, to diagnose cancer, one must learn to discriminate between benign and malignant tumors. Through examination of tumors with previously determined diagnosis, one learns some function for distinguishing the benign and malignant tumors. Then the acquired knowledge is used to diagnose new tumors. The perceptron is a simple biologically inspired model for this two-class learning problem. The perceptron is trained or constructed using examples from the two classes. Then the perceptron is used to classify new examples. We describe geometrically what a perceptron is capable of learning. Using duality, we develop a framework for investigating different methods of training a perceptron. Depending on how we define the "best" perceptron, different minimization problems are developed for training the perceptron. The effectiveness of these methods is evaluated empirically on four practical applications: breast cancer diagnosis, detection of heart disease, political voting habits, and sonar recognition. This paper does not assume prior knowledge of machine learning or pattern recognition.

Connected through hyperedge HE-1 (induced by source paper "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"):
  with members:
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Mathematical Programming in Neural Networks"
    - "Induction of Oblique Decision Trees"

Connected through hyperedge HE-2 (induced by source paper "Mathematical Programming in Neural Networks"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Bilinear Separation of Two Sets in n-Space"
    - "Feature Selection via Mathematical Programming"
    - "Backpropagation Convergence Via Deterministic Nonmonotone Perturbed Minimization"

Connected through hyperedge HE-3 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-4 (induced by source paper "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"):
  with members:
    - "Representing and Learning Visual Schemas in Neural Networks for Scene Analysis"
    - "Convergence-Zone Episodic Memory: Analysis and Simulations"
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Mean Field Theory for Sigmoid Belief Networks"
    - "Dyslexic and Category-Specific Aphasic Impairments in a Self-Organizing Feature Map Model of the Lexicon"
    - "Neural Networks and Statistical Models Proceedings of the Nineteenth Annual SAS Users Group International Conference,"
    - "Boltzmann Machine learning using mean field theory and linear response correction"
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-5 (induced by source paper "Bilinear Separation of Two Sets in n-Space"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "PATTERN RECOGNITION VIA LINEAR PROGRAMMING THEORY AND APPLICATION TO MEDICAL DIAGNOSIS"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Misclassification Minimization"
    - "Mathematical Programming in Neural Networks"
    - "Feature Selection via Mathematical Programming"

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