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: "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"
  Abstract: A paradigm of statistical mechanics of financial markets (SMFM) using nonlinear nonequilibrium algorithms, first published in L. Ingber, Mathematical Modelling, 5, 343-361 (1984), is fit to multi-variate financial markets using Adaptive Simulated Annealing (ASA), a global optimization algorithm, to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta are thereby derived and used as technical indicators in a recursive ASA optimization process to tune trading rules. These trading rules are then used on out-of-sample data, to demonstrate that they can profit from the SMFM model, to illustrate that these markets are likely not efficient.

Connected through hyperedge HE-1 (induced by source paper "Volatility of Volatility of Financial Markets"):
  with members:
    - "Canonical Momenta Indicators of Financial Markets and Neocortical EEG"
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"
    - "NONLINEAR NONEQUILIBRIUM NONQUANTUM NONCHAOTIC STATISTICAL MECHANICS OF NEOCORTICAL INTERACTIONS"

Connected through hyperedge HE-2 (induced by source paper "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"):
  with members:
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"
    - "Volatility of Volatility of Financial Markets"
    - "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"

Connected through hyperedge HE-3 (induced by source paper "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"):
  with members:
    - "PREDICTING SUNSPOTS AND EXCHANGE RATES WITH CONNECTIONIST NETWORKS"
    - "Draft Symbolic Representation of Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "POWER OF NEURAL NETS"

Connected through hyperedge HE-4 (induced by source paper "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"):
  with members:
    - "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"
    - "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"

Connected through hyperedge HE-5 (induced by source paper "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"):
  with members:
    - "STATISTICAL MECHANICS OF COMBAT WITH HUMAN FACTORS"
    - "Path-integral evolution of chaos embedded in noise: Duffing neocortical analog"
    - "MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS"

Connected through hyperedge HE-6 (induced by source paper "Path-integral evolution of chaos embedded in noise: Duffing neocortical analog"):
  with members:
    - "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"

Connected through hyperedge HE-7 (induced by source paper "NONLINEAR NONEQUILIBRIUM NONQUANTUM NONCHAOTIC STATISTICAL MECHANICS OF NEOCORTICAL INTERACTIONS"):
  with members:
    - "Volatility of Volatility of Financial Markets"

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