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: "POWER OF NEURAL NETS"
  Abstract: Report SYCON-91-11 ABSTRACT This paper deals with the simulation of Turing machines by neural networks. Such networks are made up of interconnections of synchronously evolving processors, each of which updates its state according to a "sigmoidal" linear combination of the previous states of all units. The main result states that one may simulate all Turing machines by nets, in linear time. In particular, it is possible to give a net made up of about 1,000 processors which computes a universal partial-recursive function. (This is an update of Report SYCON-91-08; new results include the simulation in linear time of binary-tape machines, as opposed to the unary alphabets used in the previous version.)

Connected through hyperedge HE-1 (induced by source paper "Can Recurrent Neural Networks Learn Natural Language Grammars? W&Z recurrent neural networks are able to"):
  with members:
    - "On the Applicability of Neural Network and Machine Learning Methodologies to Natural Language Processing"

Connected through hyperedge HE-2 (induced by source paper "Facing The Facts: Necessary Requirements For The Artificial Evolution of Complex Behaviour"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "Challenges in Evolving Controllers for Physical Robots"

Connected through hyperedge HE-3 (induced by source paper "Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks"):
  with members:
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"

Connected through hyperedge HE-4 (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"
    - "Statistical Mechanics of Nonlinear Nonequilibrium Financial Markets: Applications to Optimized Trading"

Connected through hyperedge HE-5 (induced by source paper "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"):
  with members:
    - "Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution"
    - "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"

Connected through hyperedge HE-6 (induced by source paper "Vapnik-Chervonenkis Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"
    - "On the Effect of Analog Noise in Discrete-Time Analog Computations"
    - "What Size Neural Network Gives Optimal Generalization? Convergence Properties of Backpropagation"
    - "Networks of Spiking Neurons: The Third Generation of Neural Network Models"

Connected through hyperedge HE-7 (induced by source paper "Interconnected Automata and Linear Systems: A Theoretical Framework in Discrete-Time In Hybrid Systems III: Verification"):
  with members:
    - "LINEAR SYSTEMS WITH SIGN-OBSERVATIONS"
    - "OBSERVABILITY IN RECURRENT NEURAL NETWORKS"

Connected through hyperedge HE-8 (induced by source paper "Dimension of Recurrent Neural Networks"):
  with members:
    - "Neural Networks with Quadratic VC Dimension"
    - "Sample Complexity for Learning Recurrent Perceptron Mappings"
    - "NEURAL NETS AS SYSTEMS MODELS AND CONTROLLERS suitability of "neural nets" as models for dynamical"

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