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: "incremental gradient subgradient and proximal methods for convex optimization a survey"
  Abstract: We survey incremental methods for minimizing a sum P m=1 fi(x) consisting of a large number of convex component functions fi. Our methods consist of iterations applied to single components, and have proved very effective in practice. We introduce a unified algorithmic framework for a variety of such methods, some involving gradient and subgradient iterations, which are known, and some involving combinations of subgradient and proximal methods, which are new and offer greater flexibility in exploiting the special structure of fi. We provide an analysis of the convergence and rate of convergence properties of these methods, including the advantages offered by randomization in the selection of components. We also survey applications in inference/machine learning, signal processing, and large-scale and distributed optimization.

Connected through hyperedge HE-1 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "a mean field theory of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "fixup initialization residual learning without normalization"
    - "stiffness a new perspective on generalization in neural networks"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-2 (induced by source paper "e2 train training state of the art cnns with over 80 energy savings"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "adascale towards real time video object detection using adaptive scaling"
    - "swalp stochastic weight averaging in low precision training"
    - "dual dynamic inference enabling more efficient adaptive and controllable deep inference"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-3 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "on the equivalence between quadrature rules and random features"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "scalable kernel methods via doubly stochastic gradients"

Connected through hyperedge HE-4 (induced by source paper "dtn a learning rate scheme with convergence rate of mathcal o 1 t for sgd"):
  with members:
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "theoretical properties of the global optimizer of two layer neural network"
    - "convergence analysis of two layer neural networks with relu activation"
    - "adam a method for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "minimizing age of information in a multihop wireless network"):
  with members:
    - "sampling for data freshness optimization non linear age functions"
    - "distributed scheduling algorithms for optimizing information freshness in wireless networks"
    - "content based status updates"
    - "age optimal information updates in multihop networks"
    - "status updates over unreliable multiaccess channels"
    - "the age of information real time status updating by multiple sources"

Connected through hyperedge HE-6 (induced by source paper "stochastic successive convex approximation for non convex constrained stochastic optimization"):
  with members:
    - "a parallel decomposition method for nonconvex stochastic multi agent optimization problems"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "online stochastic optimization with multiple objectives"
    - "probabilistic sinr constrained robust transmit beamforming a bernstein type inequality based conservative approach"

Connected through hyperedge HE-7 (induced by source paper "data sketching for faster training of machine learning models"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "variance reduced stochastic gradient descent with neighbors"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "the complexity of finding stationary points with stochastic gradient descent"):
  with members:
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "on the gap between strict saddles and true convexity an omega log d lower bound for eigenvector approximation"
    - "making gradient descent optimal for strongly convex stochastic optimization"

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.SY(Systems and Control)