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: "Theory Revision in Fault Hierarchies"
  Abstract: The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have developed a theory revision method that operates directly on a fault hierarchy. This task presents several challenges: A typical training instance is missing most feature values, and the pattern of missing features is significant, rather than merely an effect of noise. Moreover, the accuracy of a candidate theory is measured by considering both the sequence of tests required to arrive at a diagnosis and its agreement with the diagnostic endpoints provided by an expert. This paper first describes the algorithm for theory revision of fault hierarchies that was designed to address these challenges, then discusses its application in knowledge base maintenance and reports on experiments that use to revise a fielded diagnostic system.

Connected through hyperedge HE-1 (induced by source paper "An Optimized Theory Revision Module"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "The Complexity of Theory Revision"

Connected through hyperedge HE-2 (induced by source paper "The Challenge of Revising an Impure Theory"):
  with members:
    - "Theory Refinement Combining Analytical and Empirical Methods"
    - "The Difficulties of Learning Logic Programs with Cut"
    - "The Complexity of Theory Revision"

Connected through hyperedge HE-3 (induced by source paper "An Improved Algorithm for Incremental Induction of Decision Trees"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Integration of Case-Based Reasoning and Neural Networks Approaches for Classification"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Learning Classification Trees"
    - "Relating Relational Learning Algorithms"
    - "Geometric Comparison of Classifications and Rule Sets*"
    - "What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog"

Connected through hyperedge HE-4 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"):
  with members:
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Learning Active Classifiers"
    - "A systematic description of greedy optimisation algorithms for cost sensitive generalisation"
    - "Predicting Ordinal Classes in ILP"
    - "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"
    - "Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the"

Connected through hyperedge HE-5 (induced by source paper "Irrelevant Features and the Subset Selection Problem"):
  with members:
    - "An Optimized Theory Revision Module"
    - "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "The Effective Size of a Neural Network: A Principal Component Approach"

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