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: "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"
  Abstract: We proposed a doubly stochastic primal-dual coordinate (DSPDC) optimization algorithm for empirical risk minimization, which can be formulated as a bilinear saddle-point problem. In each iteration, our method randomly samples a block of coordinates of the primal and dual solutions to update. The convergence of our method is established in both the distance from the current iterate to the optimal solution and the primal-dual objective gap. We show that the proposed method has a lower overall complexity than existing coordinate methods when either the data matrix has a factorized structure or the proximal mapping on each block is computationally expensive, e.g., involves an eigenvalue decomposition. Furthermore, we give a theoretical lower bound on the iteration complexity of a general family of primal-dual (block) coordinate methods for bilinear saddle-point problems, which also includes DSPDC.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "theory of dual sparse regularized randomized reduction"
    - "sketching as a tool for numerical linear algebra"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "recovering the optimal solution by dual random projection"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "randomized algorithms for matrices and data"
    - "a simpler approach to matrix completion"

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)