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: "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models"
  Abstract: This work presents an application of a machine learning for characterizing an important property of natural DNA sequences compositional inhomogeneity. Compositional segments often correspond to meaningful biological units. Taking into account such inhomogeneity is a prerequisite of successful recognition of functional features in DNA sequences, especially, protein-coding genes. Here we present a technique for DNA segmentation using hidden Markov models. A DNA sequence is represented by a chain of homogeneous segments, each described by one of a few statistically discriminated hidden states, whose contents form a first-order Markov chain. The technique is used to describe and compare chromosomes I and IV of the completely sequenced Saccharomyces cerevisiae (yeast) genome. Our results indicate the existence of a few well separated states, which gives support to the isochore theory. We also explore the model's likelihood landscape and analyze the dynamics of the optimization process, thus addressing the problem of reliability of the obtained optima and efficiency of the algorithms.

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"
    - "A New Look at Tree Models for Multiple Sequence Alignment"
    - "Learning Markov chains with variable memory length from noisy output"

Connected through hyperedge HE-2 (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,"
    - "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"
    - "Gene Structure Prediction by Linguistic Methods"

Connected through hyperedge HE-3 (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,"
    - "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"
    - "A Decision Tree System for Finding Genes in DNA"
    - "A Method for Identifying Splice Sites and Translational Start Sites in"

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