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 "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"):
  Abstract: A Bayesian method for estimating the amino acid distributions in the states of a hidden Markov model (HMM) for a protein family or the columns of a multiple alignment of that family is introduced. This method uses Dirichlet mixture densities as priors over amino acid distributions. These mixture densities are determined from examination of previously constructed HMMs or multiple alignments. It is shown that this Bayesian method can improve the quality of HMMs produced from small training sets. Specific experiments on the EF-hand motif are reported, for which these priors are shown to produce HMMs with higher likelihood on unseen data, and fewer false positives and false negatives in a database search task.

Includes members:
  Member "The megaprior heuristic for discovering protein sequence patterns":
    also connected through other hyperedges:
      - 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,")
  Member "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory":
    also connected through other hyperedges:
      - 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,")
  Member "Homology Detection via Family Pairwise Search a straightforward generalization of pairwise sequence comparison algorithms to":
    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,")
  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-4 (induced by source paper "A Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions")
  Member "Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96":
    also connected through other hyperedges:
      (none)
  Member "Finding Genes in DNA with a Hidden Markov Model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Method for Identifying Splice Sites and Translational Start Sites in")
  Member "Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,")
  Member "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features")

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