human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a basic recurrent neural network model"
  Abstract: We present a model of a basic recurrent neural network (or bRNN) that includes a separate linear term with a slightly "stable" fixed matrix to guarantee bounded solutions and fast dynamic response. We formulate a state space viewpoint and adapt the constrained optimization Lagrange Multiplier (CLM) technique and the vector Calculus of Variations (CoV) to derive the (stochastic) gradient descent. In this process, one avoids the commonly used re-application of the circular chain-rule and identifies the error back-propagation with the co-state backward dynamic equations. We assert that this bRNN can successfully perform regression tracking of time-series. Moreover, the "vanishing and exploding" gradients are explicitly quantified and explained through the co-state dynamics and the update laws. The adapted CoV framework, in addition, can correctly and principally integrate new loss functions in the network on any variable and for varied goals, e.g., for supervised learning on the outputs and unsupervised learning on the internal (hidden) states.

Connected through hyperedge HE-1 (induced by source paper "slim lstm networks lstm_6 and lstm_c6"):
  with members:
    - "performance of three slim variants of the long short term memory lstm layer"
    - "slim lstms"
    - "lstm a search space odyssey"
    - "simplified long short term memory recurrent neural networks part iii"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "recurrent neural network regularization"

Connected through hyperedge HE-2 (induced by source paper "slim lstms"):
  with members:
    - "lstm a search space odyssey"
    - "simplified long short term memory recurrent neural networks part iii"
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "simplified minimal gated unit variations for recurrent neural networks"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "simplified gating in long short term memory lstm recurrent neural networks"):
  with members:
    - "lstm a search space odyssey"
    - "minimal gated unit for recurrent neural networks"
    - "theano a python framework for fast computation of mathematical expressions"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "show attend and tell neural image caption generation with visual attention"
    - "long short term memory based recurrent neural network architectures for large vocabulary speech recognition"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "recurrent neural network regularization"

Connected through hyperedge HE-4 (induced by source paper "simplified long short term memory recurrent neural networks part iii"):
  with members:
    - "simplified gating in long short term memory lstm recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning longer memory in recurrent neural networks"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.NE(Neural and Evolutionary Computing)