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 COMBAT WITH HUMAN FACTORS"
  Abstract: This highly interdisciplinary project extends previous work in combat modeling and in control-theoretic descriptions of decision-making human factors in complex activities. A previous paper has established the first theory of the statistical mechanics of combat (SMC), developed using modern methods of statistical mechanics, baselined to empirical data gleaned from the National Training Center (NTC). This previous project has also established a JANUS(T)-NTC computer simulation/wargame of NTC, providing a statistical ``what-if '' capability for NTC scenarios. This mathematical formulation is ripe for control-theoretic extension to include human factors, a methodology previously developed in the context of teleoperated vehicles. Similar NTC scenarios differing at crucial decision points will be used for data to model the inuence of decision making on combat. The results may then be used to improve present human factors and C 2 algorithms in computer simulations/wargames. Our approach is to ``subordinate'' the SMC nonlinear stochastic equations, fitted to NTC scenarios, to establish the zeroth order description of that combat. In practice, an equivalent mathematical-physics representation is used, more suitable for numerical and formal work, i.e., a Lagrangian representation. Theoretically, these equations are nested within a larger set of nonlinear stochastic operator-equations which include C 3 human factors, e.g., supervisory decisions. In this study, we propose to perturb this operator theory about the SMC zeroth order set of equations. Then, subsets of scenarios fit to zeroth order, originally considered to be similarly degenerate, can be further split perturbatively to distinguish C 3 decision-making inuences. New methods of Very Fast Simulated Re-Annealing (VFSR), developed in the previous project, will be used for fitting these models to empirical data.

Connected through hyperedge HE-1 (induced by source paper "GENETIC ALGORITHMS AND VERY FAST SIMULATED REANNEALING: A COMPARISON"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Dynamic Parameter Encoding for Genetic Algorithms"
    - "Differential Evolution A simple and efficient adaptive scheme for global optimization over continuous spaces"
    - "When Will a Genetic Algorithm Outperform Hill Climbing?"
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"

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 Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"

Connected through hyperedge HE-3 (induced by source paper "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"):
  with members:
    - "Noisy Time Series Prediction using Symbolic Representation and Recurrent Neural Network Grammatical Inference"
    - "Volatility of Volatility of Financial Markets"
    - "%A L. Ingber %T Adaptive simulated annealing (ASA): Lessons learned %J Control and Cybernetics Annealing"
    - "Path-integral evolution of chaos embedded in noise: Duffing neocortical analog"
    - "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"
    - "NONLINEAR NONEQUILIBRIUM NONQUANTUM NONCHAOTIC STATISTICAL MECHANICS OF NEOCORTICAL INTERACTIONS"

Connected through hyperedge HE-4 (induced by source paper "Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG"):
  with members:
    - "Path-integral evolution of chaos embedded in noise: Duffing neocortical analog"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"
    - "MULTIPLE SCALES OF BRAIN-MIND INTERACTIONS"

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