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 Gentle Guide to Multiple Alignment Version Please send comments, critique, flames and praise Instructions"
  Abstract: Prerequisites. An understanding of the dynamic programming (edit distance) approach to pairwise sequence alignment is useful for parts 1.3, 1.4, and 2. Also, familiarity with the use of Internet resources would be helpful for part 3. For the former, see Chapters 1.1 - 1.3, and for the latter, see Chapter 2 of the Hypertext Book of the GNA-VSNS Biocomputing Course at http://www.techfak.uni-bielefeld.de/bcd/Curric/welcome.html. General Rationale. You will understand why Multiple Alignment is considered a challenging problem, you will study approaches that try to reduce the number of steps needed to calculate the optimal solution, and you will study fast heuristics. In a case study involving immunoglobulin sequences, you will study multiple alignments obtained from WWW servers, recapitulating results from an original paper. Revision History. Version 1.01 on 17 Sep 1995. Expanded Ex.9. Updated Ex.46. Revised Solution Sheet -re- Ex.3+12. Marked more Exercises by "A" (to be submitted to the Instructor). Various minor clarifications in content

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"
    - "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"

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