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: "Generalizing from Case Studies: A Case Study"
  Abstract: Most empirical evaluations of machine learning algorithms are case studies evaluations of multiple algorithms on multiple databases. Authors of case studies implicitly or explicitly hypothesize that the pattern of their results, which often suggests that one algorithm performs significantly better than others, is not limited to the small number of databases investigated, but instead holds for some general class of learning problems. However, these hypotheses are rarely supported with additional evidence, which leaves them suspect. This paper describes an empirical method for generalizing results from case studies and an example application. This method yields rules describing when some algorithms significantly outperform others on some dependent measures. Advantages for generalizing from case studies and limitations of this particular approach are also described.

Connected through hyperedge HE-1 (induced by source paper "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Growing Simpler Decision Trees to Facilitate Knowledge Discovery"
    - "Automated model selection"
    - "Oblivious Decision Trees and Abstract Cases"
    - "The Sources of Increased Accuracy for Two Proposed Boosting Algorithms"
    - "Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise"
    - "On the Greediness of Feature Selection Algorithms"

Connected through hyperedge HE-2 (induced by source paper "Bias and the Probability of Generalization"):
  with members:
    - "Instance Pruning Techniques"

Connected through hyperedge HE-3 (induced by source paper "Dynamical Selection of Learning Algorithms"):
  with members:
    - "Dynamic Automatic Model Selection"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-4 (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"
    - "Concept Learning and Flexible Weighting"

Connected through hyperedge HE-5 (induced by source paper "Recursive Automatic Algorithm Selection for Inductive Learning"):
  with members:
    - "Multivariate versus Univariate Decision Trees"
    - "Dynamic Automatic Model Selection"
    - "Learning Polynomial Functions by Feature Construction"

Connected through hyperedge HE-6 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Constructing Nominal Xof-N Attributes"
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Draft Symbolic Representation of Neural Networks"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"

Connected through hyperedge HE-7 (induced by source paper "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"):
  with members:
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"
    - "Lookahead and Discretization in ILP"
    - "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"

Connected through hyperedge HE-8 (induced by source paper "Rule Induction with CN2: Some Recent Improvements"):
  with members:
    - "Stochastically Guided Disjunctive Version Space Learning"
    - "Using Qualitative Models to Guide Inductive Learning"
    - "Case-Based Sonogram Classification"
    - "Relating Relational Learning Algorithms"
    - "Incremental Reduced Error Pruning"
    - "What do Constructive Learners Really Learn?"
    - "Unsupervised Constructive Learning"
    - "Fossil: A Robust Relational Learner"

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