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 Decision Tree System for Finding Genes in DNA"
  Abstract: MORGAN is an integrated system for finding genes in vertebrate DNA sequences. MORGAN uses a variety of techniques to accomplish this task, the most distinctive of which is a decision tree classifier. The decision tree system is combined with new methods for identifying start codons, donor sites, and acceptor sites, and these are brought together in a frame-sensitive dynamic programming algorithm that finds the optimal segmentation of a DNA sequence into coding and noncoding regions (exons and introns). The optimal segmentation is dependent on a separate scoring function that takes a subsequence and assigns to it a score reflecting the probability that the sequence is an exon. The scoring functions in MORGAN are sets of decision trees that are combined to give a probability estimate. Experimental results on a database of 570 vertebrate DNA sequences show that MORGAN has excellent performance by many different measures. On a separate test set, it achieves an overall accuracy of 95%, with a correlation coefficient of 0.78 and a sensitivity and specificity for coding bases of 83% and 79%. In addition, MORGAN identifies 58% of coding exons exactly; i.e., both the beginning and end of the coding regions are predicted correctly. This paper describes the MORGAN system, including its decision tree routines and the algorithms for site recognition, and its performance on a benchmark database of vertebrate DNA.

Connected through hyperedge HE-1 (induced by source paper "A Method for Identifying Splice Sites and Translational Start Sites in"):
  with members:
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "Prediction of human mRNA donor and acceptor sites from the DNA sequence"

Connected through hyperedge HE-2 (induced by source paper "A Generalized Hidden Markov Model for the Recognition of Human Genes in DNA"):
  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"
    - "Finding Genes in DNA with a Hidden Markov Model"
    - "Prediction of human mRNA donor and acceptor sites from the DNA sequence"
    - "Gene Structure Prediction by Linguistic Methods"

Connected through hyperedge HE-3 (induced by source paper "A System for Induction of Oblique Decision Trees"):
  with members:
    - "Decision Tree Function Approximation in Reinforcement Learning"
    - "A hierarchical ensemble of decision trees applied to classifying data from a psychological experiment"
    - "Lazy Induction Triggered by CBR"
    - "Induction of Oblique Decision Trees"
    - "Geometry in Learning"
    - "Lookahead and Pathology in Decision Tree Induction"
    - "Decision Tree Induction: How Effective is the Greedy Heuristic?"
    - "LEARNING BY USING DYNAMIC FEATURE COMBINATION AND SELECTION"

Connected through hyperedge HE-4 (induced by source paper "Finding Genes in DNA with a Hidden Markov Model"):
  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,"
    - "Stochastic Decomposition of DNA Sequences Using Hidden Markov Models"
    - "A Generalized Hidden Markov Model for the Recognition of Human 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