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: "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"
  Abstract: Although recurrent neural nets have been moderately successful in learning to emulate finite-state machines (FSMs), the continuous internal state dynamics of a neural net are not well matched to the discrete behavior of an FSM. We describe an architecture, called DOLCE, that allows discrete states to evolve in a net as learning progresses. dolce consists of a standard recurrent neural net trained by gradient descent and an adaptive clustering technique that quantizes the state space. dolce is based on the assumption that a finite set of discrete internal states is required for the task, and that the actual network state belongs to this set but has been corrupted by noise due to inaccuracy in the weights. dolce learns to recover the discrete state with maximum a posteriori probability from the noisy state. Simulations show that dolce leads to a significant improvement in generalization performance over earlier neural net approaches to FSM induction.

Connected through hyperedge HE-1 (induced by source paper "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"):
  with members:
    - "Pruning Recurrent Neural Networks for Improved Generalization Performance"
    - "Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns"
    - "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"
    - "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"

Connected through hyperedge HE-2 (induced by source paper "Rule Revision with Recurrent Neural Networks"):
  with members:
    - "Constructing Deterministic Finite-State Automata in Recurrent Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "Learning Context-free Grammars: Capabilities and Limitations of a Recurrent Neural Network with an External Stack Memory"

Connected through hyperedge HE-3 (induced by source paper "A Stochastic Search Approach to Grammar Induction"):
  with members:
    - "4 Implementing Application Specific Routines  Genetic algorithms in search, optimization, and machine learning. Reading, MA: Addison-Wesley."
    - "A Sampling-Based Heuristic for Tree Search Applied to Grammar Induction"
    - "A Survey of Evolution Strategies"
    - "Evolution of Non-Deterministic Incremental Algorithms as a New Approach for Search in State Spaces"

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