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 Sequential Tasks by Incrementally Adding Higher Orders"
  Abstract: An incremental, higher-order, non-recurrent network combines two properties found to be useful for learning sequential tasks: higher-order connections and incremental introduction of new units. The network adds higher orders when needed by adding new units that dynamically modify connection weights. Since the new units modify the weights at the next time-step with information from the previous step, temporal tasks can be learned without the use of feedback, thereby greatly simplifying training. Furthermore, a theoretically unlimited number of units can be added to reach into the arbitrarily distant past. Experiments with the Reber grammar have demonstrated speedups of two orders of magnitude over recurrent networks.

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"
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "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 "The Functional Transfer of Knowledge for Coronary Artery Disease Diagnosis"):
  with members:
    - "Transfer of Learning by Composing Solutions of Elemental Sequential Tasks"
    - "Learning One More Thing"
    - "The Task Rehearsal Method of Sequential Learning"

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 Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"

Connected through hyperedge HE-4 (induced by source paper "Sequence Learning with Incremental Higher-Order Neural Networks"):
  with members:
    - "Induction of Multiscale Temporal Structure"

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