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: "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
  Abstract: In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued distances between instances, and attaches weights to the instances to further modify the structure of feature space. We show that this technique produces excellent classification accuracy on three problems that have been studied by machine learning researchers: predicting protein secondary structure, identifying DNA promoter sequences, and pronouncing English text. Direct experimental comparisons with the other learning algorithms show that our nearest neighbor algorithm is comparable or superior in all three domains. In addition, our algorithm has advantages in training speed, simplicity, and perspicuity. We conclude that experimental evidence favors the use and continued development of nearest neighbor algorithms for domains such as the ones studied here.

Connected through hyperedge HE-1 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems"):
  with members:
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"
    - "Logarithmic-Time Updates and Queries in Probabilistic Networks"

Connected through hyperedge HE-2 (induced by source paper "Dynamical Selection of Learning Algorithms"):
  with members:
    - "Dynamic Automatic Model Selection"
    - "Generalizing from Case Studies: A Case Study"

Connected through hyperedge HE-3 (induced by source paper "Inductive Bias in Case-Based Reasoning Systems"):
  with members:
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Yardstick for the Evaluation of Case-Based Classifiers"

Connected through hyperedge HE-4 (induced by source paper "RAPID DEVELOPMENT OF NLP MODULES WITH MEMORY-BASED LEARNING"):
  with members:
    - "Fast NP Chunking Using Memory-Based Learning Techniques"
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "Memory-Based Lexical Acquisition and Processing"
    - "Language-Independent Data-Oriented Grapheme-to-Phoneme Conversion"

Connected through hyperedge HE-5 (induced by source paper "Resolving PP attachment Ambiguities with Memory-Based Learning"):
  with members:
    - "GENERALIZATION PERFORMANCE OF BACKPROPAGATION LEARNING ON A SYLLABIFICATION TASK"
    - "Memory-Based Lexical Acquisition and Processing"

Connected through hyperedge HE-6 (induced by source paper "The Utility of Feature Weighting in Nearest-Neighbor Algorithms"):
  with members:
    - "Irrelevant Features and the Subset Selection Problem"
    - "Bias Plus Variance Decomposition for Zero-One Loss Functions"

Connected through hyperedge HE-7 (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-8 (induced by source paper "Exemplar-based Music Structure Recognition"):
  with members:
    (none)

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: Neural_Networks