human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Induction of Multiscale Temporal Structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "REDUCED MEMORY REPRESENTATIONS FOR MUSIC")
  Member "Learning Sequential Tasks by Incrementally Adding Higher Orders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Induction of Multiscale Temporal Structure")
  Member "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Rule Revision with Recurrent Neural Networks")

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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