human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distributed learning with random features"):
  Abstract: Distributed learning and random projections are the most common techniques in large scale nonparametric statistical learning. In this paper, we study the generalization properties of kernel ridge regression using both distributed methods and random features. Theoretical analysis shows the combination remarkably reduces computational cost while preserving the optimal generalization accuracy under standard assumptions. In a benign case, $\mathcal{O}(\sqrt{N})$ partitions and $\mathcal{O}(\sqrt{N})$ random features are sufficient to achieve $\mathcal{O}(1/N)$ learning rate, where $N$ is the labeled sample size. Further, we derive more refined results by using additional unlabeled data to enlarge the number of partitions and by generating features in a data-dependent way to reduce the number of random features.

Includes members:
  Member "orthogonal random features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "distributed learning with regularized least squares":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalised lipschitz regularisation equals distributional robustness")
  Member "faster kernel ridge regression using sketching and preconditioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "serverless straggler mitigation using local error correcting codes")
  Member "distributed mean estimation with limited communication":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rpn a residual pooling network for efficient federated learning")
  Member "on the equivalence between kernel quadrature rules and random feature expansions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional spectral kernel learning")
  Member "fastfood approximate kernel expansions in loglinear time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")

Question: Please tell me which class the source paper 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)