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: "Modeling Volatility using State Space Models"
  Abstract: In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensional hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude due to their ignoring the distinction between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities.

Connected through hyperedge HE-1 (induced by source paper "Learning networks for face analysis and synthesis"):
  with members:
    - "Active Gesture Recognition using Partially Observable Markov Decision Processes"
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Equivalence of Linear Boltzmann Chains and Hidden Markov Models sequence L, is: where Z(; A;"
    - "Learning from incomplete data"
    - "PRIOR KNOWLEDGE AND THE CREATION OF "VIRTUAL" EXAMPLES FOR RBF NETWORKS 1"
    - "Regularization Theory and Neural Networks Architectures"
    - "In Advances in Neural Information Processing Systems 8  Gaussian Processes for Regression"
    - "Efficient Implementation of Gaussian Processes"

Connected through hyperedge HE-2 (induced by source paper "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"):
  with members:
    - "TO IMPROVE FORECASTING"

Connected through hyperedge HE-3 (induced by source paper "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines"):
  with members:
    - "Learning networks for face analysis and synthesis"
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "A Sparse Representation for Function Approximation"
    - "Support Vector Machines: Training and Applications"
    - "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"

Connected through hyperedge HE-4 (induced by source paper "TO IMPROVE FORECASTING"):
  with members:
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"
    - "A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series"

Connected through hyperedge HE-5 (induced by source paper "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"):
  with members:
    - "First experiments using a mixture of nonlinear experts for time series prediction"
    - "Non-linear Models for Time Series Using Mixtures of Experts"
    - "Forecasting electricity demand using nonlinear mixture of experts"
    - "Introduction to Radial Basis Function Networks"
    - "Segmenting Time Series using Gated Experts with Simulated Annealing"
    - "NONLINEAR TRADING MODELS THROUGH SHARPE RATIO MAXIMIZATION"

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