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: "Using Sampling and Queries to Extract Rules from Trained Neural Networks"
  Abstract: Concepts learned by neural networks are difficult to understand because they are represented using large assemblages of real-valued parameters. One approach to understanding trained neural networks is to extract symbolic rules that describe their classification behavior. There are several existing rule-extraction approaches that operate by searching for such rules. We present a novel method that casts rule extraction not as a search problem, but instead as a learning problem. In addition to learning from training examples, our method exploits the property that networks can be efficiently queried. We describe algorithms for extracting both conjunctive and M -of-N rules, and present experiments that show that our method is more efficient than conventional search-based approaches.

Connected through hyperedge HE-1 (induced by source paper "Extracting Provably Correct Rules from Artificial Neural Networks"):
  with members:
    - "Constructing Fuzzy Graphs from Examples"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Generalization and Exclusive Allocation of Credit in Unsupervised Category Learning"):
  with members:
    - "References "Using Neural Networks to Identify Jets", Kohonen, "Self Organized Formation of Topologically Correct Feature"
    - "An information-maximisation approach to blind separation and blind deconvolution"
    - "Plasticity in cortical neuron properties: Modeling the effects of an NMDA antagonist and a GABA"
    - "Rearrangement of receptive field topography after intracortical and peripheral stimulation: The role of plasticity in"

Connected through hyperedge HE-3 (induced by source paper "The Effective Size of a Neural Network: A Principal Component Approach"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-4 (induced by source paper "Learning Symbolic Rules Using Artificial Neural Networks"):
  with members:
    - "Automatic Parameter Selection by Minimizing Estimated Error"
    - "Submitted to the Future Generation Computer Systems special issue on Data Mining. Using Neural Networks"
    - "Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability,"

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