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: "theory of deep learning iib optimization properties of sgd"
  Abstract: In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper is theoretical and experimental evidence for the following conjecture about SGD: SGD concentrates in probability -- like the classical Langevin equation -- on large volume, "flat" minima, selecting flat minimizers which are with very high probability also global minimizers

Connected through hyperedge HE-1 (induced by source paper "feature robustness flatness and generalization error for deep neural networks"):
  with members:
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "three factors influencing minima in sgd"
    - "exploring generalization in deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-2 (induced by source paper "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "network compression using correlation analysis of layer responses"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi view intact space learning"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-4 (induced by source paper "theory iii dynamics and generalization in deep networks a simple solution"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "loss landscapes of regularized linear autoencoders"
    - "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"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-5 (induced by source paper "on the bias variance tradeoff textbooks need an update"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "deep double descent where bigger models and more data hurt"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "measuring the intrinsic dimension of objective landscapes"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"

Connected through hyperedge HE-6 (induced by source paper "theoretical issues in deep networks approximation optimization and generalization"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "how to escape saddle points efficiently"
    - "theory ii landscape of the empirical risk in deep learning"

Connected through hyperedge HE-7 (induced by source paper "a modern take on the bias variance tradeoff in neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-8 (induced by source paper "theory of deep learning iii explaining the non overfitting puzzle"):
  with members:
    - "exploring generalization in deep learning"
    - "fisher rao metric geometry and complexity of neural networks"
    - "sharp minima can generalize for deep nets"
    - "theory ii landscape of the empirical risk in deep learning"
    - "understanding deep learning requires rethinking generalization"
    - "optimal rates for multi pass stochastic gradient methods"
    - "deep vs shallow networks an approximation theory perspective"
    - "identity matters in deep 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.LG(Machine Learning)