human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups papers that share a common citing paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Learning networks for face analysis and synthesis")
  Member "Segmenting Time Series using Gated Experts with Simulated Annealing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Nonlinear gated experts for time series: discovering regimes and avoiding overfitting")
  Member "Predicting Time Series with Support Vector Machines":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "Extracting Support Data for a Given Task")
  Member "Analysis of Drifting Dynamics with Neural Network Hidden Markov Models":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the citing paper (i.e., the source paper of the center hyperedge) 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