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: "stochastic gradient descent learns state equations with nonlinear activations"
  Abstract: We study discrete time dynamical systems governed by the state equation $h_{t+1}=\phi(Ah_t+Bu_t)$. Here $A,B$ are weight matrices, $\phi$ is an activation function, and $u_t$ is the input data. This relation is the backbone of recurrent neural networks (e.g. LSTMs) which have broad applications in sequential learning tasks. We utilize stochastic gradient descent to learn the weight matrices from a finite input/state trajectory $(u_t,h_t)_{t=0}^N$. We prove that SGD estimate linearly converges to the ground truth weights while using near-optimal sample size. Our results apply to increasing activations whose derivatives are bounded away from zero. The analysis is based on i) a novel SGD convergence result with nonlinear activations and ii) careful statistical characterization of the state vector. Numerical experiments verify the fast convergence of SGD on ReLU and leaky ReLU in consistence with our theory.

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"
    - "learning compact neural networks with regularization"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "on the convergence rate of training recurrent neural networks"

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:
    - "on the convergence rate of training recurrent neural networks"
    - "a local regret in nonconvex online learning"
    - "learning with random learning rates"
    - "stable recurrent models"
    - "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 "denoising and regularization via exploiting the structural bias of convolutional generators"):
  with members:
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "algorithmic guarantees for inverse imaging with untrained network priors"
    - "regularizing linear inverse problems with convolutional neural networks"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "rate optimal denoising with deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "towards moderate overparameterization global convergence guarantees for training shallow neural networks"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "on the local minima of the empirical risk"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "the global optimization geometry of shallow linear neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "gradient descent aligns the layers of deep linear networks"

Connected through hyperedge HE-5 (induced by source paper "quickly finding the best linear model in high dimensions"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "learning compact neural networks with regularization"
    - "robust one bit compressed sensing with non gaussian measurements"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "robust one bit compressed sensing with partial circulant matrices"
    - "the generalized lasso for sub gaussian measurements with dithered quantization"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-6 (induced by source paper "learning linear dynamical systems with semi parametric least squares"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "spectral filtering for general linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "variance reduction methods for sublinear reinforcement learning"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "finite time identification in unstable linear systems"
    - "learning linear dynamical systems via spectral filtering"

Connected through hyperedge HE-7 (induced by source paper "fitting relus via sgd and quantized sgd"):
  with members:
    - "learning relu networks via alternating minimization"
    - "gradiveq vector quantization for bandwidth efficient gradient aggregation in distributed cnn training"
    - "learning one convolutional layer with overlapping patches"
    - "signsgd compressed optimisation for non convex problems"
    - "learning relus via gradient descent"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "learning depth three neural networks in polynomial time"

Connected through hyperedge HE-8 (induced by source paper "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "rate optimal denoising with deep neural networks"
    - "nonconvex robust low rank matrix recovery"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "learning relu networks via alternating minimization"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"

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)