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: "gradient hard thresholding pursuit for sparsity constrained optimization"
  Abstract: Hard Thresholding Pursuit (HTP) is an iterative greedy selection procedure for finding sparse solutions of underdetermined linear systems. This method has been shown to have strong theoretical guarantee and impressive numerical performance. In this paper, we generalize HTP from compressive sensing to a generic problem setup of sparsity-constrained convex optimization. The proposed algorithm iterates between a standard gradient descent step and a hard thresholding step with or without debiasing. We prove that our method enjoys the strong guarantees analogous to HTP in terms of rate of convergence and parameter estimation accuracy. Numerical evidences show that our method is superior to the state-of-the-art greedy selection methods in sparse logistic regression and sparse precision matrix estimation tasks.

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "dual averaging method for online graph structured sparsity"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient regret minimization in non convex games"
    - "graph sparse logistic regression"
    - "learning with feature evolvable streams"
    - "unregularized online learning algorithms with general loss functions"
    - "adam a method for stochastic optimization"
    - "approximation algorithms for model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "direct acceleration of saga using sampled negative momentum"
    - "asvrg accelerated proximal svrg"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-4 (induced by source paper "best first search algorithm for non convex sparse minimization"):
  with members:
    - "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "a orthogonal matching pursuit best first search for compressed sensing signal recovery"
    - "anytime heuristic search"

Connected through hyperedge HE-5 (induced by source paper "discrimination aware channel pruning for deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "on the projection operator to a three view cardinality constrained set"):
  with members:
    - "approximation algorithms for model based compressive sensing"
    - "a totally unimodular view of structured sparsity"
    - "structured sparsity through convex optimization"
    - "model based compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"):
  with members:
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"

Connected through hyperedge HE-8 (induced by source paper "training skinny deep neural networks with iterative hard thresholding methods"):
  with members:
    - "learning both weights and connections for efficient neural networks"
    - "fast convnets using group wise brain damage"
    - "deep residual learning for image recognition"
    - "compression of deep convolutional neural networks for fast and low power mobile applications"
    - "structured pruning of deep convolutional neural networks"
    - "exploiting linear structure within convolutional networks for efficient evaluation"
    - "fitnets hints for thin deep nets"
    - "going deeper with convolutions"

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)