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: "Incremental Class Learning approach and its application to Handwritten Digit Recognition"
  Abstract: Incremental Class Learning (ICL) provides a feasible framework for the development of scalable learning systems. Instead of learning a complex problem at once, ICL focuses on learning subproblems incrementally, one at a time | using the results of prior learning for subsequent learning | and then combining the solutions in an appropriate manner. With respect to multi-class classification problems, the ICL approach presented in this paper can be summarized as follows. Initially the system focuses on one category. After it learns this category, it tries to identify a compact subset of features (nodes) in the hidden layers, that are crucial for the recognition of this category. The system then freezes these crucial nodes (features) by fixing their incoming weights. As a result, these features cannot be obliterated in subsequent learning. These frozen features are available during subsequent learning and can serve as parts of weight structures build to recognize other categories. As more categories are learned, the set of features gradually stabilizes and learning a new category requires less effort. Eventually, learning a new category may only involve combining existing features in an appropriate manner. The approach promotes the sharing of learned features among a number of categories and also alleviates the well-known catastrophic interference problem. We present results of applying the ICL approach to the Handwritten Digit Recognition problem, based on a spatio-temporal representation of patterns.

Connected through hyperedge HE-1 (induced by source paper "Learning One More Thing"):
  with members:
    - "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"
    - "The Canonical Distortion Measure for Vector Quantization and Function Approximation"
    - "Learning Model Bias minimum number of examples requred to learn a single task, and O(a"

Connected through hyperedge HE-2 (induced by source paper "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"):
  with members:
    - "Topography And Ocular Dominance: A Model Exploring Positive Correlations"
    - "Constrained Optimization for Neural Map Formation: A Unifying Framework for Weight Growth and Normalization"
    - "Self-Organization and Functional Role of Lateral Connections and Multisize Receptive Fields in the Primary Visual Cortex"
    - "Generative Models for Discovering Sparse Distributed Representations"
    - "SCRIPT RECOGNITION WITH HIERARCHICAL FEATURE MAPS"
    - "SELF-ORGANIZING PROCESS BASED ON LATERAL INHIBITION AND SYNAPTIC RESOURCE REDISTRIBUTION"
    - "IEEE Learning the Semantic Similarity of Reusable Software Components"
    - "Self-Organization and Segmentation in a Laterally Connected Orientation Map of Spiking Neurons"

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