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: "Error-Correcting Output Codes: A General Method for Improving Multiclass Inductive Learning Programs"
  Abstract: Multiclass learning problems involve finding a definition for an unknown function f(x) whose range is a discrete set containing k &gt; 2 values (i.e., k "classes"). The definition is acquired by studying large collections of training examples of the form hx i ; f(x i )i. Existing approaches to this problem include (a) direct application of multiclass algorithms such as the decision-tree algorithms ID3 and CART, (b) application of binary concept learning algorithms to learn individual binary functions for each of the k classes, and (c) application of binary concept learning algorithms with distributed output codes such as those employed by Sejnowski and Rosenberg in the NETtalk system. This paper compares these three approaches to a new technique in which BCH error-correcting codes are employed as a distributed output representation. We show that these output representations improve the performance of ID3 on the NETtalk task and of backpropagation on an isolated-letter speech-recognition task. These results demonstrate that error-correcting output codes provide a general-purpose method for improving the performance of inductive learning programs on multiclass problems.

Connected through hyperedge HE-1 (induced by source paper "Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms"):
  with members:
    - "A Statistical Approach to Decision Tree Modeling"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"

Connected through hyperedge HE-2 (induced by source paper "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"):
  with members:
    - "A System for Induction of Oblique Decision Trees"
    - "A decision-theoretic generalization of on-line learning and an application to boosting how the weight-update rule"
    - "Generating Accurate and Diverse Members of a Neural-Network Ensemble"

Connected through hyperedge HE-3 (induced by source paper "Probability Estimation via Error-Correcting Output Coding"):
  with members:
    (none)

Connected through hyperedge HE-4 (induced by source paper "A Comparative Study of ID3 and Backpropagation for English Text-to-Speech Mapping"):
  with members:
    - "Experiments on the Transfer of Knowledge between Neural Networks Reprinted from: Computational Learning Theory and"
    - "Statistical Tests for Comparing Supervised Classification Learning Algorithms"
    - "Constructing Nominal Xof-N Attributes"
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Framework for Combining Symbolic and Neural Learning rule extraction from neural networks the KBANN algorithm"
    - "Mingers, 1989 J. Mingers. An empirical comparison of pruning methods for decision tree induction. Machine"
    - "Draft Symbolic Representation of Neural Networks"
    - "Learning the Past Tense of English Verbs: The Symbolic Pattern Associator vs. Connectionist Models"

Connected through hyperedge HE-5 (induced by source paper "Error-Correcting Output Codes for Local Learners"):
  with members:
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"

Connected through hyperedge HE-6 (induced by source paper "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"):
  with members:
    - "Improving accuracy by combining rule-based and case-based reasoning"
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "Memory-Based Lexical Acquisition and Processing"
    - "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"
    - "Automatic Phonetic Transcription of Words Based On Sparse Data"

Connected through hyperedge HE-7 (induced by source paper "Error-Correcting Output Coding Corrects Bias and Variance"):
  with members:
    - "Using Decision Trees to Improve Case-Based Learning"
    - "Combining estimates in regression and classification"

Connected through hyperedge HE-8 (induced by source paper "Proben1 A Set of Neural Network Benchmark Problems and Benchmarking Rules"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Interpretable Neural Networks with BP-SOM"
    - "Avoiding Overfitting with BP-SOM"
    - "A Quantitative Study of Experimental Evaluations of Neural Network Learning Algorithms: Current Research Practice"
    - "Comparing Adaptive and Non-Adaptive Connection Pruning With Pure Early Stopping"
    - "A BENCHMARK FOR CLASSIFIER LEARNING"
    - "Adaptive Parameter Pruning in Neural Networks"
    - "An Evolutionary Method to Find Good Building-Blocks for Architectures of Artificial Neural Networks"

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