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: "The megaprior heuristic for discovering protein sequence patterns"
  Abstract: Several computer algorithms for discovering patterns in groups of protein sequences are in use that are based on fitting the parameters of a statistical model to a group of related sequences. These include hidden Markov model (HMM) algorithms for multiple sequence alignment, and the MEME and Gibbs sampler algorithms for discovering motifs. These algorithms are sometimes prone to producing models that are incorrect because two or more patterns have been combined. The statistical model produced in this situation is a convex combination (weighted average) of two or more different models. This paper presents a solution to the problem of convex combinations in the form of a heuristic based on using extremely low variance Dirichlet mixture priors as part of the statistical model. This heuristic, which we call the megaprior heuristic, increases the strength (i.e., decreases the variance) of the prior in proportion to the size of the sequence dataset. This causes each column in the final model to strongly resemble the mean of a single component of the prior, regardless of the size of the dataset. We describe the cause of the convex combination problem, analyze it mathematically, motivate and describe the implementation of the megaprior heuristic, and show how it can effectively eliminate the problem of convex combinations in protein sequence pattern discovery.

Connected through 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,"):
  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"
    - "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-2 (induced by source paper "Minimum-Risk Profiles of Protein Families Based on Statistical Decision Theory"):
  with members:
    - "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"
    - "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-3 (induced by source paper "Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families"):
  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,"
    - "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