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: "Concept Learning and Flexible Weighting"
  Abstract: We previously introduced an exemplar model, named GCM-ISW, that exploits a highly flexible weighting scheme. Our simulations showed that it records faster learning rates and higher asymptotic accuracies on several artificial categorization tasks than models with more limited abilities to warp input spaces. This paper extends our previous work; it describes experimental results that suggest human subjects also invoke such highly flexible schemes. In particular, our model provides significantly better fits than models with less flexibility, and we hypothesize that humans selectively weight attributes depending on an item's location in the input space. We need more flexible models Many theories of human concept learning posit that concepts are represented by prototypes (Reed, 1972) or exemplars (Medin & Schaffer, 1978). Prototype models represent concepts by the "best example" or "central tendency" of the concept. 1 A new item belongs in a category C if it is relatively similar to C's prototype. Prototype models are relatively inflexible; they discard a great deal of information that people use during concept learning (e.g., the number of exemplars in a concept (Homa & Cultice, 1984), the variability of features (Fried & Holyoak, 1984), correlations between features (Medin et al., 1982), and the particular exemplars used (Whittlesea, 1987)). of concept learning

Connected through hyperedge HE-1 (induced by source paper "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"):
  with members:
    - "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"
    - "Dynamic Automatic Model Selection"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-2 (induced by source paper "Case-Based Sonogram Classification"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Using Decision Trees to Improve Case-Based Learning"

Connected through hyperedge HE-3 (induced by source paper "The Management of Context-Sensitive Features: A Review of Strategies"):
  with members:
    - "Context-Sensitive Feature Selection for Lazy Learners"
    - "Recognition and Exploitation of Contextual Clues via Incremental Meta-Learning (Extended Version)"
    - "A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm"

Connected through hyperedge HE-4 (induced by source paper "Improving Minority Class Prediction Using Case-Specific Feature Weights"):
  with members:
    (none)

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: Case_Based