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 Hypothesis-driven Constructive Induction Approach to Expanding Neural Networks"
  Abstract: With most machine learning methods, if the given knowledge representation space is inadequate then the learning process will fail. This is also true with methods using neural networks as the form of the representation space. To overcome this limitation, an automatic construction method for a neural network is proposed. This paper describes the BP-HCI method for a hypothesis-driven constructive induction in a neural network trained by the backpropagation algorithm. The method searches for a better representation space by analyzing the hypotheses generated in each step of an iterative learning process. The method was applied to ten problems, which include, in particular, exclusive-or, MONK2, parity-6BIT and inverse parity-6BIT problems. All problems were successfully solved with the same initial set of parameters; the extension of representation space was no more than necessary extension for each problem.

Connected through hyperedge HE-1 (induced by source paper "Evolution of the Topology and the Weights of Neural Networks using Genetic Programming with a"):
  with members:
    - "Soft Computing: the Convergence of Emerging Reasoning Technologies"
    - "Genetic Encoding Strategies for Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Discovering Representation Space Transformations for Learning Concept Descriptions Combining DNF and M-of-N Rules"):
  with members:
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Proceedings of the First International Workshop on Intelligent Adaptive Systems (IAS-95) Constructive Induction-based Learning Agents:"
    - "Parity: The Problem that Won't Go Away"

Connected through hyperedge HE-3 (induced by source paper "What do Constructive Learners Really Learn?"):
  with members:
    - "Rule Induction with CN2: Some Recent Improvements"
    - "ID2-of-3: Constructive Induction of M of-N Concepts for Discriminators in Decision Trees"
    - "Constructive Induction Using a Non-Greedy Strategy for Feature Selection"

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