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: "learning compact neural networks with regularization"
  Abstract: We study the impact of regularization for learning neural networks. Our goal is speeding up training, improving generalization performance, and training compact models that are cost efficient. Our results apply to weight-sharing (e.g.~convolutional), sparsity (i.e.~pruning), and low-rank constraints among others. We first introduce covering dimension of the constraint set and provide a Rademacher complexity bound providing insights on generalization properties. Then, we propose and analyze regularized gradient descent algorithms for learning shallow networks. We show that problem becomes well conditioned and local linear convergence occurs once the amount of data exceeds covering dimension (e.g.~\# of nonzero weights). Finally, we provide insights on layerwise training of deep models by studying a random activation model. Our results show how regularization can be beneficial to overcome overparametrization.

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"
    - "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 "quickly finding the best linear model in high dimensions"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "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-3 (induced by source paper "deep declarative networks a new hope"):
  with members:
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "differentiable convex optimization layers"
    - "deep equilibrium models"
    - "neural ordinary differential equations"
    - "imposing hard constraints on deep networks promises and limitations"
    - "discriminatively learned hierarchical rank pooling networks"
    - "optnet differentiable optimization as a layer in neural networks"
    - "on differentiating parameterized argmin and argmax problems with application to bi level optimization"

Connected through hyperedge HE-4 (induced by source paper "a convergence theory for deep learning via over parameterization"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the convergence rate of training recurrent neural networks"
    - "learning long term dependencies via fourier recurrent units"
    - "spectral filtering for general linear dynamical systems"

Connected through hyperedge HE-5 (induced by source paper "learning compressed transforms with low displacement rank"):
  with members:
    - "non parametric transformation networks"
    - "rotation equivariant vector field networks"
    - "geometric deep learning going beyond euclidean data"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "learning compact recurrent neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "end to end learning of a convolutional neural network via deep tensor decomposition"):
  with members:
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "a provably correct algorithm for deep learning that actually works"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"

Connected through hyperedge HE-7 (induced by source paper "the generalized lasso for sub gaussian measurements with dithered quantization"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "learning without concentration"
    - "living on the edge phase transitions in convex programs with random data"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

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)