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 "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"):
  Abstract: Hidden Markov Models (HMMs) are applied to the problems of statistical modeling, database searching and multiple sequence alignment of protein families and protein domains. These methods are demonstrated on the globin family, the protein kinase catalytic domain, and the EF-hand calcium binding motif. In each case the parameters of an HMM are estimated from a training set of unaligned sequences. After the HMM is built, it is used to obtain a multiple alignment of all the training sequences. It is also used to search the SWISS-PROT 22 database for other sequences that are members of the given protein family, or contain the given domain. The HMM produces multiple alignments of good quality that agree closely with the alignments produced by programs that incorporate three-dimensional structural information. When employed in discrimination tests (by examining how closely the sequences in a database fit the globin, kinase and EF-hand HMMs), the HMM is able to distinguish members of these families from non-members with a high degree of accuracy. Both the HMM and PRO-FILESEARCH (a technique used to search for relationships between a protein sequence and multiply aligned sequences) perform better in these tests than PROSITE (a dictionary of sites and patterns in proteins). The HMM appears to have a slight advantage

Includes members:
  Member "The megaprior heuristic for discovering protein sequence patterns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory")
  Member "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems")
  Member "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The megaprior heuristic for discovering protein sequence patterns")
  Member "Homology Detection via Family Pairwise Search a straightforward generalization of pairwise sequence comparison algorithms to":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "The megaprior heuristic for discovering protein sequence patterns")
  Member "A Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions":
    also connected through other hyperedges:
      (none)
  Member "Density Networks and their Application to Protein Modelling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "A Practical Bayesian Framework for Backprop Networks")
  Member "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA")
  Member "A New Look at Tree Models for Multiple Sequence Alignment":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)")

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