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: "block coordinate frank wolfe optimization for structural svms"
  Abstract: We propose a randomized block-coordinate variant of the classic Frank-Wolfe algorithm for convex optimization with block-separable constraints. Despite its lower iteration cost, we show that it achieves a similar convergence rate in duality gap as the full Frank-Wolfe algorithm. We also show that, when applied to the dual structural support vector machine (SVM) objective, this yields an online algorithm that has the same low iteration complexity as primal stochastic subgradient methods. However, unlike stochastic subgradient methods, the block-coordinate Frank-Wolfe algorithm allows us to compute the optimal step-size and yields a computable duality gap guarantee. Our experiments indicate that this simple algorithm outperforms competing structural SVM solvers.

Connected through hyperedge HE-1 (induced by source paper "safe screening for the generalized conditional gradient method"):
  with members:
    - "deep frank wolfe for neural network optimization"
    - "multi task learning as multi objective optimization"
    - "joint screening tests for lasso"
    - "tight bounds for approximate carath eodory and beyond"
    - "the ordered weighted ell_1 norm atomic formulation projections and algorithms"
    - "a safe screening rule for sparse logistic regression"
    - "safe screening with variational inequalities and its application to lasso"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-2 (induced by source paper "curvature of feasible sets in offline and online optimization"):
  with members:
    - "introduction to online convex optimization"
    - "acceleration through optimistic no regret dynamics"
    - "faster rates for convex concave games"
    - "parameter free online learning via model selection"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "on the universality of online mirror descent"

Connected through hyperedge HE-3 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"

Connected through hyperedge HE-4 (induced by source paper "training neural networks for and by interpolation"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "deep frank wolfe for neural network optimization"

Connected through hyperedge HE-5 (induced by source paper "introduction to online convex optimization"):
  with members:
    - "online linear optimization via smoothing"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "large scale convex minimization with a low rank constraint"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "improved regret bounds for projection free bandit convex optimization"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "kernel based methods for bandit convex optimization"
    - "an optimal algorithm for bandit convex optimization"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "a hybrid algorithm for convex semidefinite optimization"
    - "projection free online learning"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-7 (induced by source paper "fully decentralized joint learning of personalized models and collaboration graphs"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "djam distributed jacobi asynchronous method for learning personal models"
    - "personalized and private peer to peer machine learning"
    - "federated multi task learning"
    - "decentralized collaborative learning of personalized models over networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"

Connected through hyperedge HE-8 (induced by source paper "efficient projection free online methods with stochastic recursive gradient"):
  with members:
    - "introduction to online convex optimization"
    - "momentum based variance reduction in non convex sgd"
    - "quantized frank wolfe faster optimization lower communication and projection free"
    - "online learning a comprehensive survey"
    - "variance reduced and projection free stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "projection free online learning"
    - "optimal distributed online prediction using mini batches"

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)