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: "A Framework for Multiple-Instance Learning"
  Abstract: Multiple-instance learning is a variation on supervised learning, where the task is to learn a concept given positive and negative bags of instances. Each bag may contain many instances, but a bag is labeled positive even if only one of the instances in it falls within the concept. A bag is labeled negative only if all the instances in it are negative. We describe a new general framework, called Diverse Density, for solving multiple-instance learning problems. We apply this framework to learn a simple description of a person from a series of images (bags) containing that person, to a stock selection problem, and to the drug activity prediction problem.

Connected through hyperedge HE-1 (induced by source paper "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"):
  with members:
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Generalizing from Case Studies: A Case Study"
    - "A Note on Learning from Multiple-Instance Examples"
    - "Lookahead and Discretization in ILP"
    - "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"

Connected through hyperedge HE-2 (induced by source paper "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
    - "A Note on Learning from Multiple-Instance Examples"

Connected through hyperedge HE-3 (induced by source paper "A Note on Learning from Multiple-Instance Examples"):
  with members:
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"
    - "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"

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