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 Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"
  Abstract: We present a statistical model of genes in DNA. A Generalized Hidden Markov Model (GHMM) provides the framework for describing the grammar of a legal parse of a DNA sequence (Stormo & Haussler 1994). Probabilities are assigned to transitions between states in the GHMM and to the generation of each nucleotide base given a particular state. Machine learning techniques are applied to optimize these probabilities using a standardized training set. Given a new candidate sequence, the best parse is deduced from the model using a dynamic programming algorithm to identify the path through the model with maximum probability. The GHMM is flexible and modular, so new sensors and additional states can be inserted easily. In addition, it provides simple solutions for integrating cardinality constraints, reading frame constraints, "indels", and homology searching. The description and results of an implementation of such a gene-finding model, called Genie, is presented. The exon sensor is a codon frequency model conditioned on windowed nucleotide frequency and the preceding codon. Two neural networks are used, as in (Brunak, Engelbrecht, & Knudsen 1991), for splice site prediction. We show that this simple model performs quite well. For a cross-validated standard test set of 304 genes [ftp://www-hgc.lbl.gov/pub/genesets] in human DNA, our gene-finding system identified up to 85% of protein-coding bases correctly with a specificity of 80%. 58% of exons were exactly identified with a specificity of 51%. Genie is shown to perform favorably compared with several other gene-finding systems.

Connected through hyperedge HE-1 (induced by source paper "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"):
  with members:
    - "The megaprior heuristic for discovering protein sequence patterns"
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory"
    - "Homology Detection via Family Pairwise Search a straightforward generalization of pairwise sequence comparison algorithms to"
    - "A Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions"
    - "Density Networks and their Application to Protein Modelling"
    - "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models"
    - "A New Look at Tree Models for Multiple Sequence Alignment"

Connected through hyperedge HE-2 (induced by source paper "Gene Structure Prediction by Linguistic Methods"):
  with members:
    - "Prediction of human mRNA donor and acceptor sites from the DNA sequence"

Connected through hyperedge HE-3 (induced by source paper "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models"):
  with members:
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"
    - "Finding Genes in DNA with a Hidden Markov Model"

Connected through hyperedge HE-4 (induced by source paper "A Decision Tree System for Finding Genes in DNA"):
  with members:
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "A System for Induction of Oblique Decision Trees"
    - "A Method for Identifying Splice Sites and Translational Start Sites in"

Connected through hyperedge HE-5 (induced by source paper "Finding Genes in DNA with a Hidden Markov Model"):
  with members:
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "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"
    - "A Decision Tree System for Finding Genes in DNA"
    - "A Method for Identifying Splice Sites and Translational Start Sites in"

Connected through hyperedge HE-6 (induced by source paper "Prediction of human mRNA donor and acceptor sites from the DNA sequence"):
  with members:
    - "A Method for Identifying Splice Sites and Translational Start Sites in"
    - "Gene Structure Prediction by Linguistic Methods"
    - "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