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 Errors to Create Piecewise Learnable Partitions"
  Abstract: In this paper we describe an algorithm which exploits the error distribution generated by a learning algorithm in order to break up the domain which is being approximated into piecewise learnable partitions. Traditionally, the error distribution has been neglected in favor of a lump error measure such as RMS. By doing this, however, we lose a lot of important information. The error distribution tells us where the algorithm is doing badly, and if there exists a "ridge" of errors, also tells us how to partition the space so that one part of the space will not interfere with the learning of another. The algorithm builds a variable arity k-d tree whose leaves contain the partitions. Using this tree, new points can be predicted using the correct partition by traversing the tree. We instantiate this algorithm using memory based learners and cross-validation.

Connected through hyperedge HE-1 (induced by source paper "Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation"):
  with members:
    - "A Statistical Approach to Solving the EBL Utility Problem"
    - "Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation"

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