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: "stochastic variance reduced optimization for nonconvex sparse learning"
  Abstract: We propose a stochastic variance reduced optimization algorithm for solving a class of large-scale nonconvex optimization problems with cardinality constraints, and provide sufficient conditions under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. We further extend our analysis to an asynchronous variant of the approach, and demonstrate a near linear speedup in sparse settings. Numerical experiments demonstrate the efficiency of our method in terms of both parameter estimation and computational performance.

Connected through hyperedge HE-1 (induced by source paper "noodl provable online dictionary learning and sparse coding"):
  with members:
    - "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"
    - "a dictionary based generalization of robust pca part i study of theoretical properties"
    - "target based hyperspectral demixing via generalized robust pca"
    - "a dictionary based generalization of robust pca"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-2 (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"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"

Connected through hyperedge HE-3 (induced by source paper "on maximization of weakly modular functions guarantees of multi stage algorithms tractability and hardness"):
  with members:
    - "beyond submodular maximization"
    - "fast parallel algorithms for feature selection"
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "an exponential speedup in parallel running time for submodular maximization without loss in approximation"
    - "greed is still good maximizing monotone submodular supermodular functions"
    - "fast greedy algorithms for dictionary selection with generalized sparsity constraints"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"

Connected through hyperedge HE-4 (induced by source paper "dual iterative hard thresholding from non convex sparse minimization to non smooth concave maximization"):
  with members:
    - "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"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"

Connected through hyperedge HE-5 (induced by source paper "nonconvex sparse learning via stochastic optimization with progressive variance reduction"):
  with members:
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "iterative hard thresholding for compressed sensing"

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)