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: "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
  Abstract: This is a brief annotated bibliography that I wanted to make available to the attendees of my Machine Learning tutorial at the 1995 AI & Statistics Workshop. These slides are available in my WWW pages under slides. Please contact me if you have any questions. Please also note the date (listed above) on which this was most recently updated. While I plan to make occasional updates to this file, it is bound to be outdated quickly. Also, I apologize for the lack of figures, but my time on this project is limited and the slides should compensate. Finally, this bibliography is, by definition, This book is now out of date. Both Pat Langley and Tom Mitchell are in the process of writing textbooks on this subject, but we're still waiting for them. Until then, I suggest looking at both the Readings and the recent ML conference proceedings (both International and European). There are also a few introductory papers on this subject, though I haven't gotten around to putting them in here yet. However, Pat Langley and Dennis Kibler (1988) have written a good paper on ML as an empirical science, and Pat has written several editorials of use to the ML author (Langley 1986; 1987; 1990). incomplete, and I've left out many other references that may be of some use.

Connected through hyperedge HE-1 (induced by source paper "(1994); Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Case-Based Reasoning: Foundational Issues, Methodological"):
  with members:
    - "Using a Case Base of Surfaces to Speed-Up Reinforcement Learning"
    - "Computer-Supported Argumentation for Cooperative Design on the World-Wide Web"
    - "Lazy Acquisition of Place Knowledge"
    - "Cooperative Bayesian and Case-Based Reasoning for Solving Multiagent Planning Tasks"
    - "SaxEx a case-based reasoning system for generating expressive musical performances"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "NACODAE: Navy Conversational Decision Aids Environment"
    - "Distance Induction in First Order Logic used for classification via a k-nearest-neighbor process. Experiments on"

Connected through hyperedge HE-2 (induced by source paper "Dynamic Automatic Model Selection"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Learning Polynomial Functions by Feature Construction"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Dynamical Selection of Learning Algorithms"
    - "Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality"

Connected through hyperedge HE-3 (induced by source paper "Concept Learning and Flexible Weighting"):
  with members:
    - "Case-Based Sonogram Classification"
    - "The Management of Context-Sensitive Features: A Review of Strategies"
    - "Improving Minority Class Prediction Using Case-Specific Feature Weights"

Connected through hyperedge HE-4 (induced by source paper "Generalizing from Case Studies: A Case Study"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Bias and the Probability of Generalization"
    - "Systematic Evaluation of Design Decisions in CBR Systems"
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

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