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: "Apple Tasting and Nearly One-Sided Learning"
  Abstract: In the standard on-line model the learning algorithm tries to minimize the total number of mistakes made in a series of trials. On each trial the learner sees an instance, either accepts or rejects that instance, and then is told the appropriate response. We define a natural variant of this model ("apple tasting") where the learner gets feedback only when the instance is accepted. We use two transformations to relate the apple tasting model to an enhanced standard model where false acceptances are counted separately from false rejections. We present a strategy for trading between false acceptances and false rejections in the standard model. From one perspective this strategy is exactly optimal, including constants. We apply our results to obtain a good general purpose apple tasting algorithm as well as nearly optimal apple tasting algorithms for a variety of standard classes, such as conjunctions and disjunctions of n boolean variables. We also present and analyze a simpler transformation useful when the instances are drawn at random rather than selected by an adversary.

Connected through hyperedge HE-1 (induced by source paper "Sequential PAC Learning"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"

Connected through hyperedge HE-2 (induced by source paper "Online Learning versus O*ine Learning"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "The Power of Self-Directed Learning"
    - "The Weighted Majority Algorithm"

Connected through hyperedge HE-3 (induced by source paper "On-line Learning with Linear Loss Constraints"):
  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