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: "distributed learning with regularized least squares"
  Abstract: We study distributed learning with the least squares regularization scheme in a reproducing kernel Hilbert space (RKHS). By a divide-and-conquer approach, the algorithm partitions a data set into disjoint data subsets, applies the least squares regularization scheme to each data subset to produce an output function, and then takes an average of the individual output functions as a final global estimator or predictor. We show with error bounds in expectation in both the $L^2$-metric and RKHS-metric that the global output function of this distributed learning is a good approximation to the algorithm processing the whole data in one single machine. Our error bounds are sharp and stated in a general setting without any eigenfunction assumption. The analysis is achieved by a novel second order decomposition of operator differences in our integral operator approach. Even for the classical least squares regularization scheme in the RKHS associated with a general kernel, we give the best learning rate in the literature.

Connected through hyperedge HE-1 (induced by source paper "generalised lipschitz regularisation equals distributional robustness"):
  with members:
    - "naive feature selection sparsity in naive bayes"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs"):
  with members:
    - "empirical risk minimization for stochastic convex optimization o 1 n and o 1 n 2 type of risk bounds"
    - "divide and conquer local average regression"
    - "refined error bounds for several learning algorithms"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"
    - "optimistic rates for learning with a smooth loss"

Connected through hyperedge HE-3 (induced by source paper "distributed learning with dependent samples"):
  with members:
    - "realization of spatial sparseness by deep relu nets with massive data"
    - "divide and conquer local average regression"
    - "optimality guarantees for distributed statistical estimation"

Connected through hyperedge HE-4 (induced by source paper "adaptive stopping rule for kernel based gradient descent algorithms"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data"):
  with members:
    - "realizing data features by deep nets"
    - "deep neural networks for rotation invariance approximation and learning"
    - "generalization and expressivity for deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep neural networks for rotation invariance approximation and learning"):
  with members:
    - "realizing data features by deep nets"
    - "generalization and expressivity for deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "deep nets for local manifold learning"

Connected through hyperedge HE-7 (induced by source paper "fast polynomial kernel classification for massive data"):
  with members:
    - "learning rates for classification with gaussian kernels"
    - "training neural networks without gradients a scalable admm approach"
    - "unregularized online learning algorithms with general loss functions"
    - "going deeper with convolutions"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "distributed learning with random features"):
  with members:
    - "orthogonal random features"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "distributed mean estimation with limited communication"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "fastfood approximate kernel expansions in loglinear time"

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)