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: "Finding Genes in DNA with a Hidden Markov Model"
  Abstract: This study describes a new Hidden Markov Model (HMM) system for segmenting uncharacterized genomic DNA sequences into exons, introns, and intergenic regions. Separate HMM modules were designed and trained for specific regions of DNA: exons, introns, intergenic regions, and splice sites. The models were then tied together to form a biologically feasible topology. The integrated HMM was trained further on a set of eukaryotic DNA sequences, and tested by using it to segment a separate set of sequences. The resulting HMM system, which is called VEIL (Viterbi Exon-Intron Locator), obtains an overall accuracy on test data of 92% of total bases correctly labelled, with a correlation coefficient of 0.73. Using the more stringent test of exact exon prediction, VEIL correctly located both ends of 53% of the coding exons, and 49% of the exons it predicts are exactly correct. These results compare favorably to the best previous results for gene structure prediction, and demonstrate the benefits of using HMMs for this problem.

Connected through hyperedge HE-1 (induced by source paper "A Method for Identifying Splice Sites and Translational Start Sites in"):
  with members:
    - "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 "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-3 (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"
    - "A Decision Tree System for Finding Genes in DNA"
    - "Prediction of human mRNA donor and acceptor sites from the DNA sequence"
    - "Gene Structure Prediction by Linguistic Methods"

Connected through hyperedge HE-4 (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,"
    - "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"

Connected through hyperedge HE-5 (induced by source paper "A Decision Tree System for Finding Genes in DNA"):
  with members:
    - "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"
    - "A System for Induction of Oblique Decision Trees"
    - "A Method for Identifying Splice Sites and Translational Start Sites in"

Connected through hyperedge HE-6 (induced by source paper "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"):
  with members:
    - "The megaprior heuristic for discovering protein sequence patterns"
    - "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"
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"
    - "Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96"
    - "Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology"
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"

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