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: "Using Prior Knowledge in an NNPDA to Learn Context-Free Languages"
  Abstract: Although considerable interest has been shown in language inference and automata induction using recurrent neural networks, success of these models has mostly been limited to regular languages. We have previously demonstrated that Neural Network Pushdown Automaton (NNPDA) model is capable of learning deterministic context-free languages (e.g., a n b n and parenthesis languages) from examples. However, the learning task is computationally intensive. In this paper we discuss some ways in which a priori knowledge about the task and data could be used for efficient learning. We also observe that such knowledge is often an experimental prerequisite for learning nontrivial languages (eg. a n b n cb m a m ).

Connected through hyperedge HE-1 (induced by source paper "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"):
  with members:
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Rule Revision with Recurrent Neural Networks"
    - "AN ADAPTIVE NEURAL NETWORK PARSER"

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