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: "Gene Structure Prediction by Linguistic Methods"
  Abstract: The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries. Parameters on the grammar rules are optimized for several different species, and mixing experiments performed to determine the degree of species specificity and the relative importance of compositional, signal-based, and syntactic components in gene prediction.

Connected through hyperedge HE-1 (induced by source paper "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"):
  with members:
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"
    - "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models"
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Prediction of human mRNA donor and acceptor sites from the DNA sequence"

Connected through hyperedge HE-2 (induced by source paper "Prediction of human mRNA donor and acceptor sites from the DNA sequence"):
  with members:
    - "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"
    - "A Method for Identifying Splice Sites and Translational Start Sites in"
    - "DNA Sequence Classification Using Compression-Based Induction"
    - "that fits the asymptotics of the problem. References"

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