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: "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"
  Abstract: This paper presents first results of an interdisciplinary project in scientific data mining. We analyze data about the carcinogenicity of chemicals derived from the carcinogenesis bioassay program performed by the US National Institute of Environmental Health Sciences. The database contains detailed descriptions of 6823 tests performed with more than 330 compounds and animals of different species, strains and sexes. The chemical structures are described at the atom and bond level, and in terms of various relevant structural properties. The goal of this paper is to investigate the effects that various levels of detail and amounts of information have on the resulting hypotheses, both quantitatively and qualitatively. We apply relational and propositional machine learning algorithms to learning problems formulated as regression or as classification tasks. In addition, these experiments have been conducted with two learning problems which are at different levels of detail. Quantitatively, our experiments indicate that additional information not necessarily improves accuracy. Qualitatively, a number of potential discoveries have been made by the algorithm for Relational Regression because it can utilize all the information contained in the relations of the database as well as in the numerical dependent variable.

Connected through hyperedge HE-1 (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"
    - "Learning Trees and Rules with Set-valued Features"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "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-2 (induced by source paper "Generating Declarative Language Bias for Top-Down ILP Algorithms"):
  with members:
    - "Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning"
    - "A Comparison of Pruning Methods for Relational Concept Learning"
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
    - "Top-down Induction of Logical Decision Trees"

Connected through hyperedge HE-3 (induced by source paper "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Learning Active Classifiers"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"

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