human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Mistake-Driven Learning in Text Categorization"):
  Abstract: Learning problems in the text processing domain often map the text to a space whose dimensions are the measured features of the text, e.g., its words. Three characteristic properties of this domain are (a) very high dimensionality, (b) both the learned concepts and the instances reside very sparsely in the feature space, and (c) a high variation in the number of active features in an instance. In this work we study three mistake-driven learning algorithms for a typical task of this nature - text categorization. We argue that these algorithms which categorize documents by learning a linear separator in the feature space have a few properties that make them ideal for this domain. We then show that a quantum leap in performance is achieved when we further modify the algorithms to better address some of the specific characteristics of the domain. In particular, we demonstrate (1) how variation in document length can be tolerated by either normalizing feature weights or by using negative weights, (2) the positive effect of applying a threshold range in training, (3) alternatives in considering feature frequency, and (4) the benefits of discarding features while training. Overall, we present an algorithm, a variation of Littlestone's Winnow, which performs significantly better than any other algorithm tested on this task using a similar feature set.

Includes members:
  Member "How to Use Expert Advice (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Update rules for parameter estimation in Bayesian networks")
  Member "Context-sensitive learning methods for text categorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Using and combining predictors that specialize")
  Member "Applying Winnow to Context-Sensitive Spelling Correction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Active Learning with Committees for Text Categorization")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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