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: "A New Look at Tree Models for Multiple Sequence Alignment"
  Abstract: Evolutionary trees are frequently used as the underlying model in the design of algorithms, optimization criteria and software packages for multiple sequence alignment (MSA). In this paper, we reexamine the suitability of trees as a universal model for MSA in light of the broad range of biological questions that MSA's are used to address. A tree model consists of a tree topology and a model of accepted mutations along the branches. After surveying the major applications of MSA, examples from the molecular biology literature are used to illustrate situations in which this tree model fails. This occurs when the relationship between residues in a column cannot be described by a tree; for example, in some structural and functional applications of MSA. It also occurs in situations, such as lateral gene transfer, where an entire gene cannot be modeled by a unique tree. In cases of nonparsimonous data or convergent evolution, it may be difficult to find a consistent mutational model. We hope that this survey will promote dialogue between biologists and computer scientists, leading to more biologically realistic research on MSA.

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:
    - "The megaprior heuristic for discovering protein sequence patterns"
    - "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"
    - "Learning Markov chains with variable memory length from noisy output"

Connected through hyperedge HE-2 (induced by source paper "On the Approximability of Numerical Taxonomy (Fitting Distances by Tree Metrics)"):
  with members:
    - "TREE CONTRACTIONS AND EVOLUTIONARY TREES"
    - "The Disk-Covering Method for Tree Reconstruction"
    - "Efficient Algorithms for Inverting Evolution"
    - "Constructing Big Trees from Short Sequences"

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