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: "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
  Abstract: We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints. Sufficient conditions are provided, under which the proposed algorithm enjoys strong linear convergence guarantees and optimal estimation accuracy in high dimensions. We further extend the proposed algorithm to an asynchronous parallel variant with a near linear speedup. Numerical experiments demonstrate the efficiency of our algorithm in terms of both parameter estimation and computational performance.

Connected through hyperedge HE-1 (induced by source paper "interaction hard thresholding consistent sparse quadratic regression in sub quadratic time and space"):
  with members:
    - "high dimensional robust sparse regression"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "a greedy approach for budgeted maximum inner product search"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "sparse polynomial learning and graph sketching"
    - "compressed matrix multiplication"

Connected through hyperedge HE-2 (induced by source paper "vr sgd a simple stochastic variance reduction method for machine learning"):
  with members:
    - "accelerated variance reduced stochastic admm"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "deep learning with elastic averaging sgd"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-3 (induced by source paper "larger is better the effect of learning rates enjoyed by stochastic optimization with progressive variance reduction"):
  with members:
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "accelerated variance reduced stochastic admm"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "improved svrg for non strongly convex or sum of non convex objectives"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"

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)