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 Active Classifiers"
  Abstract: Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can | at some cost | obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework | which is perhaps the main contribution of this paper | we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable.

Connected through hyperedge HE-1 (induced by source paper "Learning to Reason with a Restricted View"):
  with members:
    - "Cognitive Computation (Extended Abstract)"
    - "On the Hardness of Approximate Reasoning"

Connected through hyperedge HE-2 (induced by source paper "Learning Default Concepts"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Probably Approximately Optimal Derivation Strategies"
    - "ON THE SAMPLE COMPLEXITY OF FINDING GOOD SEARCH STRATEGIES 2n trials of each undetermined experiment"
    - "Knowing What Doesn't Matter: Exploiting Omitted Superfluous Data"

Connected through hyperedge HE-3 (induced by source paper "Exploiting the Omission of Irrelevant Data"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "Boosting Trees for Cost-Sensitive Classifications"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "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 "Theory and Applications of Agnostic PAC-Learning with Small Decision Trees"):
  with members:
    - "Pessimistic decision tree pruning based on tree size"
    - "Error-Based and Entropy-Based Discretization of Continuous Features"
    - "Stochastic Propositionalization of Non-Determinate Background Knowledge"
    - "Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail"

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