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: "flexible multilayer sparse approximations of matrices and applications"
  Abstract: The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizing the corresponding matrix into few sparse factors. The approach relies on recent advances in nonconvex optimization. It is first explained and analyzed in details and then demonstrated experimentally on various problems including dictionary learning for image denoising and the approximation of large matrices arising in inverse problems.

Connected through hyperedge HE-1 (induced by source paper "learning fast algorithms for linear transforms using butterfly factorizations"):
  with members:
    - "neural arithmetic logic units"
    - "quadrature based features for kernel approximation"
    - "learning compressed transforms with low displacement rank"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "strassennets deep learning with a multiplication budget"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "neural combinatorial optimization with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "quick means acceleration of k means by learning a fast transform"):
  with members:
    - "revisiting the nystrom method for improved large scale machine learning"
    - "randomized dimensionality reduction for k means clustering"

Connected through hyperedge HE-3 (induced by source paper "multilayer convolutional sparse modeling pursuit and dictionary learning"):
  with members:
    - "convolutional dictionary learning via local processing"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "online convolutional dictionary learning"
    - "trainlets dictionary learning in high dimensions"
    - "maximal sparsity with deep networks"
    - "on the expressive power of deep learning a tensor analysis"
    - "winner take all autoencoders"
    - "k sparse autoencoders"

Connected through hyperedge HE-4 (induced by source paper "approximate fast graph fourier transforms via multilayer sparse approximations"):
  with members:
    - "fast orthonormal sparsifying transforms based on householder reflectors"
    - "trainlets dictionary learning in high dimensions"
    - "compressive spectral clustering"
    - "gspbox a toolbox for signal processing on graphs"
    - "powers of tensors and fast matrix multiplication"
    - "discrete signal processing on graphs frequency analysis"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-5 (induced by source paper "stable safe screening and structured dictionaries for faster ell _ 1 regularization"):
  with members:
    - "screening tests for lasso problems"
    - "joint screening tests for lasso"
    - "trainlets dictionary learning in high dimensions"
    - "separable dictionary learning"
    - "safe screening with variational inequalities and its application to lasso"
    - "lasso screening rules via dual polytope projection"
    - "safe feature elimination in sparse supervised learning"

Connected through hyperedge HE-6 (induced by source paper "analyzing the approximation error of the fast graph fourier transform"):
  with members:
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-7 (induced by source paper "scalable m channel critically sampled filter banks for graph signals"):
  with members:
    - "approximate fast graph fourier transforms via multilayer sparse approximations"
    - "structured sampling and fast reconstruction of smooth graph signals"
    - "fast eigenspace approximation using random signals"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "an m channel critically sampled filter bank for graph signals"
    - "subgraph based filterbanks for graph signals"
    - "signals on graphs uncertainty principle and sampling"
    - "random sampling of bandlimited signals on graphs"

Connected through hyperedge HE-8 (induced by source paper "sketching for large scale learning of mixture models"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "a la carte learning fast kernels"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "orthogonal matching pursuit with replacement"

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)