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: "robust frequent directions with application in online learning"
  Abstract: The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms.

Connected through hyperedge HE-1 (induced by source paper "a deterministic streaming sketch for ridge regression"):
  with members:
    - "efficient frequent directions algorithm for sparse matrices"
    - "online row sampling"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "low rank approximation and regression in input sparsity time"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-2 (induced by source paper "federated pca with adaptive rank estimation"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cola decentralized linear learning"
    - "moses a streaming algorithm for linear dimensionality reduction"
    - "differentially private federated learning a client level perspective"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"

Connected through hyperedge HE-3 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction"):
  with members:
    - "what to expect when you are expecting on the grassmannian"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "streaming principal component analysis from incomplete data"
    - "weighted matrix completion and recovery with prior subspace information"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"

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)