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: "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"
  Abstract: This work describes an approach for inferring Deterministic Context-free (DCF) Grammars in a Connectionist paradigm using a Recurrent Neural Network Pushdown Automaton (NNPDA). The NNPDA consists of a recurrent neural network connected to an external stack memory through a common error function. We show that the NNPDA is able to learn the dynamics of an underlying pushdown automaton from examples of grammatical and non-grammatical strings. Not only does the network learn the state transitions in the automaton, it also learns the actions required to control the stack. In order to use continuous optimization methods, we develop an analog stack which reverts to a discrete stack by quantization of all activations, after the network has learned the transition rules and stack actions. We further show an enhancement of the network's learning capabilities by providing hints. In addition, an initial comparative study of simulations with first, second and third order recurrent networks has shown that the increased degree of freedom in a higher order networks improve generalization but not necessarily learning speed.

Connected through hyperedge HE-1 (induced by source paper "Rule Revision with Recurrent Neural Networks"):
  with members:
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"

Connected through hyperedge HE-2 (induced by source paper "Using Prior Knowledge in an NNPDA to Learn Context-Free Languages"):
  with members:
    (none)

Connected through hyperedge HE-3 (induced by source paper "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"):
  with members:
    - "Induction of Multiscale Temporal Structure"
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"

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