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 Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
  Abstract: We present a neural net architecture that can discover hierarchical and recursive structure in symbol strings. To detect structure at multiple levels, the architecture has the capability of reducing symbols substrings to single symbols, and makes use of an external stack memory. In terms of formal languages, the architecture can learn to parse strings in an LR(0) context-free grammar. Given training sets of positive and negative exemplars, the architecture has been trained to recognize many different grammars. The architecture has only one layer of modifiable weights, allowing for a Many cognitive domains involve complex sequences that contain hierarchical or recursive structure, e.g., music, natural language parsing, event perception. To illustrate, "the spider that ate the hairy fly" is a noun phrase containing the embedded noun phrase "the hairy fly." Understanding such multilevel structures requires forming reduced descriptions (Hinton, 1988) in which a string of symbols or states ("the hairy fly") is reduced to a single symbolic entity (a noun phrase). We present a neural net architecture that learns to encode the structure of symbol strings via such reduction transformations. The difficult problem of extracting multilevel structure from complex, extended sequences has been studied by Mozer (1992), Ring (1993), Rohwer (1990), and Schmidhuber (1992), among others. While these previous efforts have made some straightforward interpretation of its behavior.

Connected through hyperedge HE-1 (induced by source paper "Induction of Multiscale Temporal Structure"):
  with members:
    - "Book Review  Introduction to the Theory of Neural Computation Reviewed by: 2"
    - "Tau Net: A Neural Network for Modeling Temporal Variability"
    - "Neural net architectures for temporal sequence processing"
    - "REDUCED MEMORY REPRESENTATIONS FOR MUSIC"
    - "Learning Sequential Tasks by Incrementally Adding Higher Orders"
    - "Diffusion of Context and Credit Information in Markovian Models"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Learning Sequential Tasks by Incrementally Adding Higher Orders"):
  with members:
    - "Induction of Multiscale Temporal Structure"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"
    - "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"

Connected through hyperedge HE-3 (induced by source paper "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"):
  with members:
    - "Rule Revision with Recurrent Neural Networks"
    - "Using Prior Knowledge in an NNPDA to Learn Context-Free Languages"
    - "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