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: "NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all"
  Abstract: In many learning problems, the learning system is presented with values for features that are actually irrelevant to the concept it is trying to learn. The FOCUS algorithm, due to Almuallim and Dietterich, performs an explicit search for the smallest possible input feature set S that permits a consistent mapping from the features in S to the output feature. The FOCUS algorithm can also be seen as an algorithm for learning determinations or functional dependencies, as suggested in [6]. Another algorithm for learning determinations appears in [7]. The FOCUS algorithm has superpolynomial runtime, but Almuallim and Di-etterich leave open the question of tractability of the underlying problem. In this paper, the problem is shown to be NP-complete. We also describe briefly some experiments that demonstrate the benefits of determination learning, and show that finding lowest-cardinality determinations is easier in practice than finding minimal determi Define the MIN-FEATURES problem as follows: given a set X of examples (which are each composed of a a binary value specifying the value of the target feature and a vector of binary values specifying the values of the other features) and a number n, determine whether or not there exists some feature set S such that: We show that MIN-FEATURES is NP-complete by reducing VERTEX-COVER to MIN-FEATURES. 1 The VERTEX-COVER problem may be stated as the question: given a graph G with vertices V and edges E, is there a subset V 0 of V , of size m, such that each edge in E is connected to at least one vertex in V 0 ? We may reduce an instance of VERTEX-COVER to an instance of MIN-FEATURES by mapping each edge in E to an example in X, with one input feature for every vertex in V . 1 In [8], a "proof" is reported for this result by reduction to set covering. The proof therefore fails to show NP-completeness. nations.

Connected through hyperedge HE-1 (induced by source paper "Irrelevant Features and the Subset Selection Problem"):
  with members:
    - "Theory Revision in Fault Hierarchies"
    - "An Optimized Theory Revision Module"
    - "Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle"
    - "Feature Subset Selection as Search with Probabilistic Estimates"
    - "Cost-sensitive feature reduction applied to a hybrid genetic algorithm"
    - "Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms"
    - "Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome"
    - "The Effective Size of a Neural Network: A Principal Component Approach"

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