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: "safe adaptive importance sampling"
  Abstract: Importance sampling has become an indispensable strategy to speed up optimization algorithms for large-scale applications. Improved adaptive variants - using importance values defined by the complete gradient information which changes during optimization - enjoy favorable theoretical properties, but are typically computationally infeasible. In this paper we propose an efficient approximation of gradient-based sampling, which is based on safe bounds on the gradient. The proposed sampling distribution is (i) provably the best sampling with respect to the given bounds, (ii) always better than uniform sampling and fixed importance sampling and (iii) can efficiently be computed - in many applications at negligible extra cost. The proposed sampling scheme is generic and can easily be integrated into existing algorithms. In particular, we show that coordinate-descent (CD) and stochastic gradient descent (SGD) can enjoy significant a speed-up under the novel scheme. The proven efficiency of the proposed sampling is verified by extensive numerical testing.

Connected through hyperedge HE-1 (induced by source paper "adaptive sampling distributed stochastic variance reduced gradient for heterogeneous distributed datasets"):
  with members:
    - "not all samples are created equal deep learning with importance sampling"
    - "biased importance sampling for deep neural network training"
    - "federated optimization distributed machine learning for on device intelligence"
    - "online learning to sample"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-2 (induced by source paper "decentralized deep learning with arbitrary communication compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "robust and communication efficient collaborative learning"
    - "an exact quantized decentralized gradient descent algorithm"
    - "texttt deepsqueeze decentralization meets error compensated compression"

Connected through hyperedge HE-3 (induced by source paper "on linear learning with manycore processors"):
  with members:
    - "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations"
    - "partitioning compute units in cnn acceleration for statistical memory traffic shaping"
    - "faster coordinate descent via adaptive importance sampling"
    - "scaling deep learning on gpu and knights landing clusters"
    - "efficient use of limited memory accelerators for linear learning on heterogeneous systems"
    - "primal dual rates and certificates"
    - "tensorflow a system for large scale machine learning"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"

Connected through hyperedge HE-4 (induced by source paper "online variance reduction with mixtures"):
  with members:
    - "introduction to online convex optimization"
    - "not all samples are created equal deep learning with importance sampling"
    - "coordinate descent with bandit sampling"
    - "faster coordinate descent via adaptive importance sampling"
    - "prioritized experience replay"
    - "online learning to sample"
    - "online batch selection for faster training of neural networks"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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)