human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "A Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions")
  Member "Finding Genes in DNA with a Hidden Markov Model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "A Method for Identifying Splice Sites and Translational Start Sites in")
  Member "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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