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: "Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks"
  Abstract: A modified Recurrent Neural Network (RNN) is used to learn a Self-Routing Interconnection Network (SRIN) from a set of routing examples. The RNN is modified so that it has several distinct initial states. This is equivalent to a single RNN learning multiple different synchronous sequential machines. We define such a sequential machine structure as augmented and show that a SRIN is essentially an Augmented Synchronous Sequential Machine (ASSM). As an example, we learn a small six-switch SRIN. After training we extract the net-work's internal representation of the ASSM and corresponding SRIN. fl This paper is adapted from ( Goudreau, 1993, Chapter 6 ) . A shortened version of this paper was published in ( Goudreau & Giles, 1993 ) .

Connected through hyperedge HE-1 (induced by source paper "First-Order vs. Second-Order Single Layer Recurrent Neural Networks"):
  with members:
    - "POWER OF NEURAL NETS"
    - "Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution"

Connected through hyperedge HE-2 (induced by source paper "Performance of On-Line Learning Methods in Predicting Multiprocessor Memory Access Patterns"):
  with members:
    - "Hierarchical Mixtures of Experts and the EM Algorithm"
    - "Routing in Optical Multistage Interconnection Networks: a Neural Network Solution"
    - "Predictive Control of Opto-Electronic Reconfigurable Interconnection Networks Using Neural Networks"

Connected through hyperedge HE-3 (induced by source paper "Cryptographic Limitations on Learning Boolean Formulae and Finite Automata"):
  with members:
    - "Boosting a weak learning algorithm by majority To be published in Information and Computation"
    - "Learning DFA from Simple Examples"
    - "A General Lower Bound on the Number of Examples Needed for Learning"
    - "Sequential PAC Learning"
    - "PAC-Learning PROLOG clauses with or without errors"
    - "Toward Efficient Agnostic Learning"
    - "Efficient Distribution-free Learning of Probabilistic Concepts"
    - "On the Sample Complexity of Weakly Learning"

Connected through hyperedge HE-4 (induced by source paper "Pruning Recurrent Neural Networks for Improved Generalization Performance"):
  with members:
    - "A Delay Damage Model Selection Algorithm for NARX Neural Networks"
    - "Extraction of Rules from Discrete-Time Recurrent Neural Networks"
    - "Evolving Artificial Neural Networks using the Baldwin Effect"
    - "Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks"

Connected through hyperedge HE-5 (induced by source paper "A Unified Gradient-Descent/Clustering Architecture for Finite State Machine Induction"):
  with members:
    - "A Stochastic Search Approach to Grammar Induction"
    - "Rule Revision with Recurrent Neural Networks"

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