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: "Dynamical Selection of Learning Algorithms"
  Abstract: Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain have met with limited success. This paper proposes a new approach to predicting a given example's class by locating it in the "example space" and then choosing the best learner(s) in that region of the example space to make predictions. The regions of the example space are defined by the prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region. This dynamic approach to learning algorithm selection is compared to other methods for selecting from multiple learning algorithms. The approach is then extended to weight rather than select the algorithms according to their past performance in a given region. Both approaches are further evaluated on a set of Determining the conditions for which a given learning algorithm is appropriate is an open problem in machine learning. Methods for selecting a learning algorithm for a given domain (e.g. [Aha92, Breiman84]) or for a portion of the domain ([Brodley93, Brodley94]) have met with limited success. This paper proposes a new approach that dynamically selects a learning algorithm for each example by locating it in the "example space" and then choosing the best learner(s) for prediction in that part of the example space. The regions of the example space are formed by the observed prediction patterns of the learners being used. The learner(s) chosen for prediction are selected according to their past performance in that region which is defined by the "cross-validation history." This paper introduces DS, a method for the dynamic selection of a learning algorithm(s). We call it "dynamic" because the learning algorithm(s) used to classify a novel example depends on that example. Preliminary experimentation motivated DW, an extension to DS that dynamically weights the learners predictions according to their regional accuracy. Further experimentation compares DS and DW to a collection of other meta-learning strategies such as cross-validation ([Breiman84]) and various forms of stacking ([Wolpert92]). In this phase of the experiementation, the meta-learners have six constituent learners which are heterogeneous in their search and representation methods (e.g. a rule learner, CN2 [Clark89]; a decision tree learner, C4.5 [Quinlan93]; an oblique decision tree learner, OC1 [Murthy93]; an instance-based learner, PEBLS [Cost93]; a k-nearest neighbor learner, ten domains and compared to several other meta-learning strategies.

Connected through hyperedge HE-1 (induced by source paper "Dynamic Automatic Model Selection"):
  with members:
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "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"
    - "Comparison of Regression Methods, Symbolic Induction Methods and Neural Networks in Morbidity Diagnosis and Mortality"

Connected through hyperedge HE-2 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"):
  with members:
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "Resolving PP attachment Ambiguities with Memory-Based Learning"
    - "Memory-Based Lexical Acquisition and Processing"

Connected through hyperedge HE-3 (induced by source paper "Generalizing from Case Studies: A Case Study"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "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"
    - "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: Theory