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 Note on Learning from Multiple-Instance Examples"
  Abstract: We describe a simple reduction from the problem of PAC-learning from multiple-instance examples to that of PAC-learning with one-sided random classification noise. Thus, all concept classes learnable with one-sided noise, which includes all concepts learnable in the usual 2-sided random noise model plus others such as the parity function, are learnable from multiple-instance examples. We also describe a more efficient (and somewhat technically more involved) reduction to the Statistical-Query model that results in a polynomial-time algorithm for learning axis-parallel rectangles with sample complexity ~ O(d 2 r=* 2 ), saving roughly a factor of r over the results of Auer et al. (1997).

Connected through hyperedge HE-1 (induced by source paper "A Framework for Multiple-Instance Learning"):
  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"

Connected through hyperedge HE-2 (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 Framework for Multiple-Instance Learning"
    - "Lookahead and Discretization in ILP"
    - "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"

Connected through hyperedge HE-3 (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 Framework for Multiple-Instance 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