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: "Appears in Working Notes, Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms"
  Abstract: This paper presents the Plannett system, which combines artificial neural networks to achieve expert- level accuracy on the difficult scientific task of recognizing volcanos in radar images of the surface of the planet Venus. Plannett uses ANNs that vary along two dimensions: the set of input features used to train and the number of hidden units. The ANNs are combined simply by averaging their output activations. When Plannett is used as the classification module of a three-stage image analysis system called JAR- tool, the end-to-end accuracy (sensitivity and specificity) is as good as that of a human planetary geologist on a four-image test suite. JARtool-Plannett also achieves the best algorithmic accuracy on these images to date.

Connected through hyperedge HE-1 (induced by source paper "How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications"):
  with members:
    - "Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm"
    - "In Machine Learning: A Multistrategy Approach, Vol. IV  Macro and Micro Perspectives of Multistrategy 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: Neural_Networks