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: "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.

Connected through hyperedge HE-1 (induced by source paper "Applying Winnow to Context-Sensitive Spelling Correction"):
  with members:
    - "Active Learning with Committees for Text Categorization"
    - "Learning Distributions from Random Walks"

Connected through hyperedge HE-2 (induced by source paper "Context-sensitive learning methods for text categorization"):
  with members:
    - "How to Use Expert Advice (Extended Abstract)"
    - "Heterogeneous Uncertainty Sampling for Supervised Learning"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Learning Trees and Rules with Set-valued Features"
    - "Using and combining predictors that specialize"
    - "Transferring and Retraining Learned Information Filters"

Connected through hyperedge HE-3 (induced by source paper "How to Use Expert Advice (Extended Abstract)"):
  with members:
    - "Online Learning versus O*ine Learning"
    - "Simulating Access to Hidden Information while Learning"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "Context-sensitive learning methods for text categorization"
    - "WORST CASE PREDICTION OVER SEQUENCES UNDER LOG LOSS"
    - "Large Margin Classification Using the Perceptron Algorithm"
    - "An Efficient Extension to Mixture Techniques for Prediction and Decision Trees"

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