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: "stable recurrent models"
  Abstract: Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks. In this work, we conduct a thorough investigation of stable recurrent models. Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent. Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks. Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime. Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.

Connected through hyperedge HE-1 (induced by source paper "non asymptotic and accurate learning of nonlinear dynamical systems"):
  with members:
    - "finite time analysis of approximate policy iteration for the linear quadratic regulator"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "derivative free methods for policy optimization guarantees for linear quadratic systems"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning compact neural networks with regularization"
    - "global convergence of policy gradient methods for the linear quadratic regulator"

Connected through hyperedge HE-2 (induced by source paper "rnn based online learning an efficient first order optimization algorithm with a convergence guarantee"):
  with members:
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "on the convergence rate of training recurrent neural networks"
    - "a local regret in nonconvex online learning"
    - "learning with random learning rates"
    - "lstm a search space odyssey"
    - "efficient regret minimization in non convex games"
    - "training deep networks without learning rates through coin betting"
    - "gradient descent learns linear dynamical systems"

Connected through hyperedge HE-3 (induced by source paper "beyond exploding and vanishing gradients analysing rnn training using attractors and smoothness"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "language modeling with gated convolutional networks"
    - "unitary evolution recurrent neural networks"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-4 (induced by source paper "predicting 3d human dynamics from video"):
  with members:
    - "xnect real time multi person 3d human pose estimation with a single rgb camera"
    - "exploiting temporal context for 3d human pose estimation in the wild"
    - "sim2real transfer learning for 3d pose estimation motion to the rescue"
    - "deep lip reading a comparison of models and an online application"
    - "3d human pose estimation in video with temporal convolutions and semi supervised training"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "learning 3d human dynamics from video"

Connected through hyperedge HE-5 (induced by source paper "theoretical limitations of self attention in neural sequence models"):
  with members:
    - "assessing the ability of self attention networks to learn word order"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "bert rediscovers the classical nlp pipeline"
    - "on the computational power of rnns"
    - "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"
    - "open sesame getting inside bert s linguistic knowledge"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-6 (induced by source paper "customizing sequence generation with multi task dynamical systems"):
  with members:
    - "multi task time series analysis applied to drug response modelling"
    - "quaternet a quaternion based recurrent model for human motion"
    - "disentangled sequential autoencoder"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "filtering variational objectives"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-7 (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"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "quasi recurrent neural networks"
    - "pointer sentinel mixture models"
    - "a recurrent neural network without chaos"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "contracting implicit recurrent neural networks stable models with improved trainability"):
  with members:
    - "specialized interior point algorithm for stable nonlinear system identification"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "convex parameterizations and fidelity bounds for nonlinear identification and reduced order modelling"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "a simple way to initialize recurrent networks of rectified linear units"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "a differential lyapunov framework for contraction analysis"
    - "on the difficulty of training recurrent neural networks"

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)