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: "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.

Connected through hyperedge HE-1 (induced by source paper "Towards a Better Understanding of Memory-Based Reasoning Systems"):
  with members:
    - "An Analysis of Bayesian Classifiers (1988), involves the formulation of average-case models for specific algorithms"
    - "Average-Case Analysis of a Nearest Neighbor Algorithm"
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"
    - "EXPLORING A FRAMEWORK FOR INSTANCE BASED LEARNING AND NAIVE BAYESIAN CLASSIFIERS"
    - "Logarithmic-Time Updates and Queries in Probabilistic Networks"

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"
    - "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"
    - "Learning Markov chains with variable memory length from noisy output"

Connected through hyperedge HE-3 (induced by source paper "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory"):
  with members:
    - "The megaprior heuristic for discovering protein sequence patterns"
    - "Hidden Markov Models in Computational Biology: Applications to Protein Modeling UCSC-CRL-93-32 Keywords: Hidden Markov Models,"
    - "Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology"

Connected through hyperedge HE-4 (induced by source paper "The megaprior heuristic for discovering protein sequence patterns"):
  with members:
    - "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,"

Connected through hyperedge HE-5 (induced by source paper "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"):
  with members:
    - "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features"

Connected through hyperedge HE-6 (induced by source paper "Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96"):
  with members:
    (none)

Connected through hyperedge HE-7 (induced by source paper "Finding Genes in DNA with a Hidden Markov Model"):
  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 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