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: "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

Connected through hyperedge HE-1 (induced by source paper "A Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions"):
  with members:
    (none)

Connected through hyperedge HE-2 (induced by source paper "Density Networks and their Application to Protein Modelling"):
  with members:
    - "A Practical Bayesian Framework for Backprop Networks"

Connected through hyperedge HE-3 (induced by source paper "Learning Markov chains with variable memory length from noisy output"):
  with members:
    - "Learning Probabilistic Automata with Variable Memory Length"

Connected through hyperedge HE-4 (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"
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology"

Connected through hyperedge HE-5 (induced by source paper "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"):
  with members:
    - "Stochastic Decomposition of DNA Sequences Using 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-6 (induced by source paper "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models"):
  with members:
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"

Connected through hyperedge HE-7 (induced by source paper "The megaprior heuristic for discovering protein sequence patterns"):
  with members:
    - "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"

Connected through hyperedge HE-8 (induced by source paper "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"):
  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"
    - "Optimal Alignments in Linear Space using Automaton-derived Cost Functions (Extended Abstract) Submitted to CPM'96"
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "Dirichlet Mixtures: A Method for Improving Detection of Weak but Significant Protein Sequence Homology"
    - "Protein Secondary Structure Modelling with Probabilistic Networks (Extended Abstract)"
    - "Towards a Better Understanding of Memory-Based Reasoning Systems"

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