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: "Online Learning versus O*ine Learning"
  Abstract: We present an off-line variant of the mistake-bound model of learning. Just like in the well studied on-line model, a learner in the offline model has to learn an unknown concept from a sequence of elements of the instance space on which he makes "guess and test" trials. In both models, the aim of the learner is to make as few mistakes as possible. The difference between the models is that, while in the on-line model only the set of possible elements is known, in the off-line model the sequence of elements (i.e., the identity of the elements as well as the order in which they are to be presented) is known to the learner in advance. We give a combinatorial characterization of the number of mistakes in the off-line model. We apply this characterization to solve several natural questions that arise for the new model. First, we compare the mistake bounds of an off-line learner to those of a learner learning the same concept classes in the on-line scenario. We show that the number of mistakes in the on-line learning is at most a log n factor more than the off-line learning, where n is the length of the sequence. In addition, we show that if there is an off-line algorithm that does not make more than a constant number of mistakes for each sequence then there is an online algorithm that also does not make more than a constant number of mistakes. The second issue we address is the effect of the ordering of the elements on the number of mistakes of an off-line learner. It turns out that there are sequences on which an off-line learner can guarantee at most one mistake, yet a permutation of the same sequence forces him to err on many elements. We prove, however, that the gap, between the off-line mistake bounds on permutations of the same sequence of n-many elements, cannot be larger than a multiplicative factor of log n, and we present examples that obtain such a gap.

Connected through hyperedge HE-1 (induced by source paper "The Power of Self-Directed Learning"):
  with members:
    - "Teaching a Smarter Learner"
    - "An Interactive Model of Teaching"

Connected through hyperedge HE-2 (induced by source paper "The Weighted Majority Algorithm"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"
    - "The Power of Team Exploration: Two Robots Can Learn Unlabeled Directed Graphs"

Connected through hyperedge HE-4 (induced by source paper "Apple Tasting and Nearly One-Sided Learning"):
  with members:
    - "On-line Learning with Linear Loss Constraints"
    - "Sequential PAC Learning"

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