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: "Annealed Competition of Experts for a Segmentation and Classification of Switching Dynamics"
  Abstract: We present a method for the unsupervised segmentation of data streams originating from different unknown sources which alternate in time. We use an architecture consisting of competing neural networks. Memory is included in order to resolve ambiguities of input-output relations. In order to obtain maximal specialization, the competition is adiabatically increased during training. Our method achieves almost perfect identification and segmentation in the case of switching chaotic dynamics where input manifolds overlap and input-output relations are ambiguous. Only a small dataset is needed for the training proceedure. Applications to time series from complex systems demonstrate the potential relevance of our approach for time series analysis and short-term prediction.

Connected through hyperedge HE-1 (induced by source paper "Predicting Time Series with Support Vector Machines"):
  with members:
    - "Extracting Support Data for a Given Task"

Connected through hyperedge HE-2 (induced by source paper "Analysis of Drifting Dynamics with Neural Network Hidden Markov Models"):
  with members:
    (none)

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"
    - "Modeling Volatility using State Space Models"
    - "Support Vector Machines: Training and Applications"

Connected through hyperedge HE-4 (induced by source paper "Segmenting Time Series using Gated Experts with Simulated Annealing"):
  with members:
    - "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting"

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