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: "reversible recurrent neural networks"
  Abstract: Recurrent neural networks (RNNs) provide state-of-the-art performance in processing sequential data but are memory intensive to train, limiting the flexibility of RNN models which can be trained. Reversible RNNs---RNNs for which the hidden-to-hidden transition can be reversed---offer a path to reduce the memory requirements of training, as hidden states need not be stored and instead can be recomputed during backpropagation. We first show that perfectly reversible RNNs, which require no storage of the hidden activations, are fundamentally limited because they cannot forget information from their hidden state. We then provide a scheme for storing a small number of bits in order to allow perfect reversal with forgetting. Our method achieves comparable performance to traditional models while reducing the activation memory cost by a factor of 10--15. We extend our technique to attention-based sequence-to-sequence models, where it maintains performance while reducing activation memory cost by a factor of 5--10 in the encoder, and a factor of 10--15 in the decoder.

Connected through hyperedge HE-1 (induced by source paper "deep equilibrium models"):
  with members:
    - "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "implicit deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "pytorch an imperative style high performance deep learning library"
    - "recurrent stacking of layers for compact neural machine translation models"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-2 (induced by source paper "equilibrated recurrent neural network neuronal time delayed self feedback improves accuracy and stability"):
  with members:
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "approximating real time recurrent learning with random kronecker factors"
    - "reviving and improving recurrent back propagation"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "unbiased online recurrent optimization"

Connected through hyperedge HE-3 (induced by source paper "mintnet building invertible neural networks with masked convolutions"):
  with members:
    - "on the convergence proof of amsgrad and a new version"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "on the convergence of adam and beyond"
    - "emerging convolutions for generative normalizing flows"
    - "invertible residual networks"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-4 (induced by source paper "hamiltonian neural networks"):
  with members:
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural ordinary differential equations"
    - "i revnet deep invertible networks"
    - "relational inductive bias for physical construction in humans and machines"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-5 (induced by source paper "on the variance of unbiased online recurrent optimization"):
  with members:
    - "optimizing agent behavior over long time scales by transporting value"
    - "approximating real time recurrent learning with random kronecker factors"
    - "rudder return decomposition for delayed rewards"
    - "the reversible residual network backpropagation without storing activations"
    - "unbiased online recurrent optimization"
    - "training deep nets with sublinear memory cost"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "training recurrent networks online without backtracking"

Connected through hyperedge HE-6 (induced by source paper "sum of squares polynomial flow"):
  with members:
    - "autoregressive quantile networks for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"
    - "density modeling of images using a generalized normalization transformation"
    - "made masked autoencoder for distribution estimation"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-7 (induced by source paper "echo compiler based gpu memory footprint reduction for lstm rnn training"):
  with members:
    - "relay a new ir for machine learning frameworks"
    - "glow graph lowering compiler techniques for neural networks"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "eie efficient inference engine on compressed deep neural network"

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.LG(Machine Learning)