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 recurrent neural network without chaos"
  Abstract: We introduce an exceptionally simple gated recurrent neural network (RNN) that achieves performance comparable to well-known gated architectures, such as LSTMs and GRUs, on the word-level language modeling task. We prove that our model has simple, predicable and non-chaotic dynamics. This stands in stark contrast to more standard gated architectures, whose underlying dynamical systems exhibit chaotic behavior.

Connected through hyperedge HE-1 (induced by source paper "provable benefit of orthogonal initialization in optimizing deep linear networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "dynamical isometry and a mean field theory of lstms and grus"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "stable architectures for deep neural networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "depth creates no bad local minima"

Connected through hyperedge HE-2 (induced by source paper "the trade off between long term memory and smoothness for recurrent networks"):
  with members:
    - "on the smoothness of nonlinear system identification"
    - "frage frequency agnostic word representation"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "language modeling with gated convolutional networks"
    - "neural machine translation in linear time"
    - "wavenet a generative model for raw audio"
    - "pointer sentinel mixture models"

Connected through hyperedge HE-3 (induced by source paper "adaptively truncating backpropagation through time to control gradient bias"):
  with members:
    - "approximating real time recurrent learning with random kronecker factors"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "unbiasing truncated backpropagation through time"
    - "stable recurrent models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "quasi recurrent neural networks"
    - "pointer sentinel mixture models"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "indylstms independently recurrent lstms"):
  with members:
    - "fast multi language lstm based online handwriting recognition"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "quasi recurrent neural networks"
    - "neural architecture search with reinforcement learning"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "dropout improves recurrent neural networks for handwriting recognition"

Connected through hyperedge HE-5 (induced by source paper "nais net stable deep networks from non autonomous differential equations"):
  with members:
    - "can recurrent neural networks warp time"
    - "spectral normalization for generative adversarial networks"
    - "stable architectures for deep neural networks"
    - "the reversible residual network backpropagation without storing activations"
    - "maximum principle based algorithms for deep learning"
    - "probabilistic adaptive computation time"
    - "exploring neural transducers for end to end speech recognition"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-6 (induced by source paper "stable recurrent models"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "kronecker recurrent units"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "top k off policy correction for a reinforce recommender system"):
  with members:
    - "deep reinforcement learning for page wise recommendations"
    - "neural collaborative filtering"
    - "constrained policy optimization"
    - "improved recurrent neural networks for session based recommendations"
    - "unbiased learning to rank with biased feedback"
    - "asynchronous methods for deep reinforcement learning"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "safe and efficient off policy reinforcement learning"

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)