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: "projection free online learning"
  Abstract: The computational bottleneck in applying online learning to massive data sets is usually the projection step. We present efficient online learning algorithms that eschew projections in favor of much more efficient linear optimization steps using the Frank-Wolfe technique. We obtain a range of regret bounds for online convex optimization, with better bounds for specific cases such as stochastic online smooth convex optimization. #R##N#Besides the computational advantage, other desirable features of our algorithms are that they are parameter-free in the stochastic case and produce sparse decisions. We apply our algorithms to computationally intensive applications of collaborative filtering, and show the theoretical improvements to be clearly visible on standard datasets.

Connected through hyperedge HE-1 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems"):
  with members:
    - "fast stochastic algorithms for low rank and nonsmooth matrix problems"
    - "fast algorithms for robust pca via gradient descent"
    - "variance reduced and projection free stochastic optimization"
    - "matrix completion has no spurious local minimum"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "phase retrieval via matrix completion"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-2 (induced by source paper "faster projection free online learning"):
  with members:
    - "introduction to online convex optimization"
    - "linear convergence of a frank wolfe type algorithm over trace norm balls"
    - "variance reduced and projection free stochastic optimization"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"

Connected through hyperedge HE-3 (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"
    - "block coordinate frank wolfe optimization for structural svms"
    - "online bandit learning against an adaptive adversary from regret to policy regret"
    - "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-4 (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"
    - "block coordinate frank wolfe optimization for structural svms"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "online continuous submodular maximization from full information to bandit feedback"):
  with members:
    - "black box submodular maximization discrete and continuous settings"
    - "optimal dr submodular maximization and applications to provable mean field inference"
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "an optimal algorithm for bandit convex optimization"
    - "submodular functions from discrete to continous domains"
    - "online submodular maximization under a matroid constraint with application to learning assignments"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"

Connected through hyperedge HE-6 (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"
    - "block coordinate frank wolfe optimization for structural svms"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-7 (induced by source paper "stochastic recursive gradient based methods for projection free online learning"):
  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"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "block coordinate frank wolfe optimization for structural svms"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-8 (induced by source paper "apprenticeship learning via frank wolfe"):
  with members:
    - "average reward reinforcement learning with unknown mixing times"
    - "variance reduced and projection free stochastic optimization"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

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)