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: "Tau Net: A Neural Network for Modeling Temporal Variability"
  Abstract: The ability to handle temporal variation is important when dealing with real-world dynamic signals. In many applications, inputs do not come in as fixed-rate sequences, but rather as signals with time scales that can vary from one instance to the next; thus, modeling dynamic signals requires not only the ability to recognize sequences but also the ability to handle temporal changes in the signal. This paper discusses "Tau Net," a neural network for modeling dynamic signals, and its application to speech. In Tau Net, sequence learning is accomplished using a combination of prediction, recurrence and time-delay connections. Temporal variability is modeled by having adaptable time constants in the network, which are adjusted with respect to the prediction error. Adapting the time constants changes the time scale of the network, and the adapted value of the network's time constant provides a measure of temporal variation in the signal. Tau Net has been applied to several simple signals: sets of sine waves differing in frequency and in phase [2], a multidimensional signal representing the walking gait of children [3], and the energy contour of a simple speech utterance [11]. Tau Net has also been shown to work on a voicing distinction task using synthetic speech data [12]. In this paper, Tau Net is applied to two speaker-independent tasks, vowel recognition (of f/ae/,/iy/,/ux/g) and consonant recognition (of f/p/,/t/,/k/g) using speech data taken from the TIMIT database. It is shown that Tau Nets, trained on medium-rate tokens, achieved about the same performance as networks without time constants trained on tokens at all rates, and performed better than networks without time constants trained on medium-rate tokens. Our results demonstrate Tau Net's ability to identify vowels and consonants at variable speech rates by extrapolating to rates not represented in the training set.

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"
    - "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"
    - "Diffusion of Context and Credit Information in Markovian Models"
    - "Sequence Learning with Incremental Higher-Order Neural Networks"

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