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: "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"
  Abstract: Sketching techniques have become popular for scaling up machine learning algorithms by reducing the sample size or dimensionality of massive data sets, while still maintaining the statistical power of big data. In this paper, we study sketching from an optimization point of view: we first show that the iterative Hessian sketch is an optimization process with preconditioning, and develop accelerated iterative Hessian sketch via the searching the conjugate direction; we then establish primal-dual connections between the Hessian sketch and dual random projection, and apply the preconditioned conjugate gradient approach on the dual problem, which leads to the accelerated iterative dual random projection methods. Finally to tackle the challenges from both large sample size and high-dimensionality, we propose the primal-dual sketch, which iteratively sketches the primal and dual formulations. We show that using a logarithmic number of calls to solvers of small scale problem, primal-dual sketch is able to recover the optimum of the original problem up to arbitrary precision. The proposed algorithms are validated via extensive experiments on synthetic and real data sets which complements our theoretical results.

Connected through hyperedge HE-1 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-2 (induced by source paper "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"):
  with members:
    - "importance sampling for minibatches"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "communication complexity of distributed convex learning and optimization"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "scalable kernel methods via doubly stochastic gradients"
    - "semi stochastic coordinate descent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "recovering the optimal solution by dual random projection"

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)