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 Default Concepts"
  Abstract: Classical concepts, based on necessary and sufficient defining conditions, cannot classify logically insufficient object descriptions. Many reasoning systems avoid this limitation by using "default concepts" to classify incompletely described objects. We address the task of learning such default concepts from observational data. We model the underlying performance task | classifying incomplete examples | by a probabilistic process where random test examples are passed through a "blocker" that can hide object attributes from the classifier. We then address the task of learning accurate default concepts from random training examples. We survey the learning techniques that have been proposed for this task in the machine learning and knowledge representation literatures, investigate the relative merits of each, and show that a superior learning technique can be developed from well known statistical principles. Finally, we extend Valiant's pac-learning framework to this context and obtain a number of useful learnability results.

Connected through hyperedge HE-1 (induced by source paper "Learning Active Classifiers"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "Exploiting the Omission of Irrelevant Data"
    - "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"
    - "Learning to Reason with a Restricted View"

Connected through hyperedge HE-2 (induced by source paper "Probably Approximately Optimal Derivation Strategies"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning an Optimally Accurate Representational System"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-3 (induced by source paper "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Learning an Optimally Accurate Representational System"
    - "Probably Approximately Optimal Derivation Strategies"
    - "Learning Hierarchical Rule Sets"

Connected through hyperedge HE-4 (induced by source paper "A Statistical Approach to Solving the EBL Utility Problem"):
  with members:
    - "A Formal Framework for Speedup Learning from Problems and Solutions"
    - "Simple Selection of Utile Control Rules in Speedup Learning"
    - "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"
    - "Learning an Optimally Accurate Representational System"
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"

Connected through hyperedge HE-5 (induced by source paper "Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data"):
  with members:
    (none)

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