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: "Hierarchical Recurrent Neural Networks for Long-Term Dependencies"
  Abstract: We have already shown that extracting long-term dependencies from sequential data is difficult, both for deterministic dynamical systems such as recurrent networks, and probabilistic models such as hidden Markov models (HMMs) or input/output hidden Markov models (IOHMMs). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper, we propose to use a more general type of a-priori knowledge, namely that the temporal dependencies are structured hierarchically. This implies that long-term dependencies are represented by variables with a long time scale. This principle is applied to a recurrent network which includes delays and multiple time scales. Experiments confirm the advantages of such structures. A similar approach is proposed for HMMs and IOHMMs.

Connected through hyperedge HE-1 (induced by source paper "TD Models: Modeling the World at a Mixture of Time Scales"):
  with members:
    - "Planning and Learning in an Adversarial Robotic Game"
    - "Planning with Closed-Loop Macro Actions"
    - "Multi-time Models for Temporally Abstract Planning"
    - "Multi-time Models for Temporally"
    - "Multi-Time Models for Reinforcement Learning"

Connected through hyperedge HE-2 (induced by source paper "Diffusion of Context and Credit Information in Markovian Models"):
  with members:
    - "Induction of Multiscale Temporal Structure"

Connected through hyperedge HE-3 (induced by source paper "GUESSING CAN OUTPERFORM MANY LONG TIME LAG ALGORITHMS"):
  with members:
    - "DISCOVERING NEURAL NETS WITH LOW KOLMOGOROV COMPLEXITY AND HIGH GENERALIZATION CAPABILITY Neural Networks 10(5):857-873, 1997"
    - "FLAT MINIMA Neural Computation 9(1):1-42 (1997)"
    - "LEARNING COMPLEX, EXTENDED SEQUENCES USING THE PRINCIPLE OF HISTORY COMPRESSION (Neural Computation, 4(2):234-242, 1992)"
    - "LONG SHORT-TERM MEMORY Neural Computation 9(8):1735-1780, 1997"

Connected through hyperedge HE-4 (induced by source paper "Boltzmann Chains and Hidden Markov Models"):
  with members:
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Exploiting Tractable Substructures in Intractable Networks"
    - "Learning in Boltzmann Trees"
    - "Coupled hidden Markov models for complex action recognition"

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