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: "faster coordinate descent via adaptive importance sampling"
  Abstract: Coordinate descent methods employ random partial updates of decision variables in order to solve huge-scale convex optimization problems. In this work, we introduce new adaptive rules for the random selection of their updates. By adaptive, we mean that our selection rules are based on the dual residual or the primal-dual gap estimates and can change at each iteration. We theoretically characterize the performance of our selection rules and demonstrate improvements over the state-of-the-art, and extend our theory and algorithms to general convex objectives. Numerical evidence with hinge-loss support vector machines and Lasso confirm that the practice follows the theory.

Connected through hyperedge HE-1 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "the roadmap to 6g ai empowered wireless networks"
    - "improved scaling law for activity detection in massive mimo systems"
    - "joint activity detection and channel estimation for iot networks phase transition and computation estimation tradeoff"
    - "grant free massive mtc enabled massive mimo a compressive sensing approach"
    - "machine learning for combinatorial optimization a methodological tour d horizon"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"

Connected through hyperedge HE-2 (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"
    - "scaling deep learning on gpu and knights landing clusters"
    - "efficient use of limited memory accelerators for linear learning on heterogeneous systems"
    - "safe adaptive importance sampling"
    - "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-3 (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"
    - "safe 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"

Connected through hyperedge HE-4 (induced by source paper "lsh sampling breaks the computation chicken and egg loop in adaptive stochastic gradient estimation"):
  with members:
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "fast approximation of matrix coherence and statistical leverage"
    - "towards optimal one pass large scale learning with averaged stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "advances in variational inference"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "iterative amortized inference"
    - "inference suboptimality in variational autoencoders"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "the numerics of gans"
    - "stein variational policy gradient"
    - "towards deeper understanding of variational autoencoding models"

Connected through hyperedge HE-6 (induced by source paper "stochastic learning on imbalanced data determinantal point processes for mini batch diversification"):
  with members:
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "importance sampling for minibatches"
    - "learning visual features from large weakly supervised data"
    - "efficient sampling for k determinantal point processes"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (induced by source paper "safe adaptive importance sampling"):
  with members:
    - "approximate steepest coordinate descent"
    - "importance sampling for minibatches"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "safe screening with variational inequalities and its application to lasso"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"

Connected through hyperedge HE-8 (induced by source paper "coordinate descent with bandit sampling"):
  with members:
    - "stochastic optimization with bandit sampling"
    - "approximate steepest coordinate descent"
    - "importance sampling for minibatches"
    - "minding the gaps for block frank wolfe optimization of structured svms"

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)