Above are papers related to the following paper: [Paper_ID] Paper #711994. 	[Title] DYNAMICAL BEHAVIOR OF ARTIFICIAL NEURAL NETWORKS WITH RANDOM WEIGHTS [Abstract] In this paper we report a Monte Carlo study of the dynamics of large untrained, feedforward, neural networks with randomly chosen weights and feedback. The analysis consists of looking at the percent of the systems that exhibit chaos, the distrubution of largest Lyapunov exponents, and the distrubution of correlation dimensions. As the systems become more complex (increasing inputs and neurons), the probability of chaos approaches unity. The correlation dimension is typically much smaller than the system dimension. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1134197. 	[Title] On the Probability of Chaos in Large Dynamical Systems: A Monte Carlo Study [Abstract] In this paper we report the result of a Monte Carlo study on the probability of chaos in large dynamical systems. We use neural networks as the basis functions for the system dynamics and choose parameter values for the networks randomly. Our results show that as the dimension of the system and the complexity of the network increase, the probability of chaotic dynamics increases to 100%. Since neural networks are dense in the set of dynamical systems, our conclusion is that most large systems are chaotic. Please summarise all the given information.

Paper #711994 has the title `DYNAMICAL BEHAVIOR OF ARTIFICIAL NEURAL NETWORKS WITH RANDOM WEIGHTS  
` and the abstract `In this paper we report a Monte Carlo study of the dynamics of large untrained, feedforward, neural networks with randomly chosen weights and feedback. The analysis consists of looking at the percent of the systems that exhibit chaos, the distrubution of largest Lyapunov exponents, and the distrubution of correlation dimensions. As the systems become more complex (increasing inputs and neurons), the probability of chaos approaches unity. The correlation dimension is typically much smaller than the system dimension. 
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Question: Which category should Paper #711994 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #711994 has the title `DYNAMICAL BEHAVIOR OF ARTIFICIAL NEURAL NETWORKS WITH RANDOM WEIGHTS  
` and the abstract `In this paper we report a Monte Carlo study of the dynamics of large untrained, feedforward, neural networks with randomly chosen weights and feedback. The analysis consists of looking at the percent of the systems that exhibit chaos, the distrubution of largest Lyapunov exponents, and the distrubution of correlation dimensions. As the systems become more complex (increasing inputs and neurons), the probability of chaos approaches unity. The correlation dimension is typically much smaller than the system dimension. 
`.
Question: Which category should Paper #711994 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks