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: "Learning Trees and Rules with Set-valued Features"
  Abstract: In most learning systems examples are represented as fixed-length "feature vectors", the components of which are either real numbers or nominal values. We propose an extension of the feature-vector representation that allows the value of a feature to be a set of strings; for instance, to represent a small white and black dog with the nominal features size and species and the set-valued feature color, one might use a feature vector with size=small, species=canis-familiaris and color=fwhite,blackg. Since we make no assumptions about the number of possible set elements, this extension of the traditional feature-vector representation is closely connected to Blum's "infinite attribute" representation. We argue that many decision tree and rule learning algorithms can be easily extended to set-valued features. We also show by example that many real-world learning problems can be efficiently and naturally represented with set-valued features; in particular, text categorization problems and problems that arise in propositionalizing first-order representations lend themselves to set-valued features.

Connected through hyperedge HE-1 (induced by source paper "Stochastic Propositionalization of Non-Determinate Background Knowledge"):
  with members:
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "SFOIL: Stochastic Approach to Inductive Logic Programming"
    - "Inverse entailment and Progol"

Connected through hyperedge HE-2 (induced by source paper "Inverse entailment and Progol"):
  with members:
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"
    - "Predicting Ordinal Classes in ILP"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
    - "Extraction of Meta-Knowledge to Restrict the Hypothesis Space for ILP Systems incorporating them in FOIL."
    - "Which Hypotheses Can Be Found with Inverse Entailment? -Extended Abstract"
    - "Bottom-up induction of logic programs with more than one recursive clause"
    - "Relational Knowledge Discovery in Databases"

Connected through hyperedge HE-3 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Mistake-Driven Learning in Text Categorization"
    - "Using and combining predictors that specialize"
    - "Transferring and Retraining Learned Information Filters"

Connected through hyperedge HE-4 (induced by source paper "Transferring and Retraining Learned Information Filters"):
  with members:
    - "Incremental Reduced Error Pruning"
    - "Context-sensitive learning methods for text categorization"
    - "Feature Generation for Sequence Categorization"
    - "Is Learning The n-th Thing Any Easier Than Learning The First?"

Connected through hyperedge HE-5 (induced by source paper "Heterogeneous Uncertainty Sampling for Supervised Learning"):
  with members:
    - "More Efficient Windowing"
    - "Information-based objective functions for active data selection"
    - "Context-sensitive learning methods for text categorization"
    - "Query by Committee"

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: Rule_Learning