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: "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles"
  Abstract: The multiple instance problem arises in tasks where the training examples are ambiguous: a single example object may have many alternative feature vectors (instances) that describe it, and yet only one of those feature vectors may be responsible for the observed classification of the object. This paper describes and compares three kinds of algorithms that learn axis-parallel rectangles to solve the multiple-instance problem. Algorithms that ignore the multiple instance problem perform very poorly. An algorithm that directly confronts the multiple instance problem (by attempting to identify which feature vectors are responsible for the observed classifications) performs best, giving 89% correct predictions on a musk-odor prediction task. The paper also illustrates the use of artificial data to debug and compare these algorithms.

Connected through hyperedge HE-1 (induced by source paper "A Comparison of Dynamic Reposing and Tangent Distance for Drug Activity Prediction"):
  with members:
    (none)

Connected through hyperedge HE-2 (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"
    - "A Note on Learning from Multiple-Instance Examples"

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"
    - "A Note on Learning from Multiple-Instance Examples"
    - "A Framework for Multiple-Instance Learning"

Connected through hyperedge HE-4 (induced by source paper "Approximating Hyper-Rectangles: Learning and Pseudo-random Sets"):
  with members:
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "PAC Learning Axis-aligned Rectangles with Respect to Product Distributions from Multiple-instance Examples"

Connected through hyperedge HE-5 (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"
    - "A Framework for Multiple-Instance Learning"

Connected through hyperedge HE-6 (induced by source paper "Lookahead and Discretization in ILP"):
  with members:
    - "Applying ILP to Diterpene Structure Elucidation from 13 C NMR Spectra"
    - "Multi-class problems and discretization in ICL Extended abstract"
    - "Top-down Induction of Logical Decision Trees"

Connected through hyperedge HE-7 (induced by source paper "Generalizing from Case Studies: A Case Study"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "Machine Learning: An Annotated Bibliography for the 1995 AI Statistics Tutorial on Machine Learning (Version 1)"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Bias and the Probability of Generalization"
    - "Systematic Evaluation of Design Decisions in CBR Systems"
    - "Dynamical Selection of Learning Algorithms"
    - "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"

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: Neural_Networks