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: "Gain Adaptation Beats Least Squares?"
  Abstract: I present computational results suggesting that gain-adaptation algorithms based in part on connectionist learning methods may improve over least squares and other classical parameter-estimation methods for stochastic time-varying linear systems. The new algorithms are evaluated with respect to classical methods along three dimensions: asymptotic error, computational complexity, and required prior knowledge about the system. The new algorithms are all of the same order of complexity as LMS methods, O(n), where n is the dimensionality of the system, whereas least-squares methods and the Kalman filter are O(n 2 ). The new methods also improve over the Kalman filter in that they do not require a complete statistical model of how the system varies over time. In a simple computational experiment, the new methods are shown to produce asymptotic error levels near that of the optimal Kalman filter and significantly below those of least-squares and LMS methods. The new methods may perform better even than the Kalman filter if there is any error in the filter's model of how the system varies over time.

Connected through hyperedge HE-1 (induced by source paper "Least-Squares Temporal Difference Learning"):
  with members:
    - "Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction,"
    - "Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming"
    - "A Comparison of Direct and Model-Based Reinforcement Learning"

Connected through hyperedge HE-2 (induced by source paper "Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta"):
  with members:
    - "MultiPlayer Residual Advantage Learning With General Function Approximation"

Connected through hyperedge HE-3 (induced by source paper "Learning Polynomial Functions by Feature Construction"):
  with members:
    - "Multivariate Decision Trees"
    - "Recursive Automatic Algorithm Selection for Inductive Learning"
    - "Dynamic Automatic Model Selection"
    - "Classification of EEG Signals Using a Sparse Polynomial Builder"

Connected through hyperedge HE-4 (induced by source paper "From Isolation to Cooperation: An Alternative View of a System of Experts"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"

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