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: "Diffusion of Context and Credit Information in Markovian Models"
  Abstract: This paper studies the problem of ergodicity of transition probability matrices in Marko-vian models, such as hidden Markov models (HMMs), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from Markov chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm.

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"
    - "A Connectionist Symbol Manipulator That Discovers the Structure of Context-Free Languages"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"):
  with members:
    - "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"
    - "TD Models: Modeling the World at a Mixture of Time Scales"
    - "Boltzmann Chains and Hidden Markov Models"

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