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: "Investigating the Value of a Good Input Representation"
  Abstract: This paper is reprinted from Computational Learning Theory and Natural Learning Systems, vol. 3, T. Petsche, S. Judd, and S. Hanson, (eds.), forthcoming 1995. Copyrighted 1995 by MIT Press Abstract The ability of an inductive learning system to find a good solution to a given problem is dependent upon the representation used for the features of the problem. A number of factors, including training-set size and the ability of the learning algorithm to perform constructive induction, can mediate the effect of an input representation on the accuracy of a learned concept description. We present experiments that evaluate the effect of input representation on generalization performance for the real-world problem of finding genes in DNA. Our experiments that demonstrate that: (1) two different input representations for this task result in significantly different generalization performance for both neural networks and decision trees; and (2) both neural and symbolic methods for constructive induction fail to bridge the gap between these two representations. We believe that this real-world domain provides an interesting challenge problem for the machine learning subfield of constructive induction because the relationship between the two representations is well known, and because conceptually, the representational shift involved in constructing the better representation should not be too imposing.

Connected through hyperedge HE-1 (induced by source paper "Learning to Predict Reading Frames in E. coli DNA Sequences"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools"
    - "Learning to Represent Codons: A Challenge Problem for Constructive Induction"

Connected through hyperedge HE-2 (induced by source paper "Rerepresenting and Restructuring Domain Theories: A Constructive Induction Approach"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Using Qualitative Models to Guide Inductive Learning"

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