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: "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
  Abstract: Linear optimization is many times algorithmically simpler than non-linear convex optimization. Linear optimization over matroid polytopes, matching polytopes and path polytopes are example of problems for which we have simple and efficient combinatorial algorithms, but whose non-linear convex counterpart is harder and admits significantly less efficient algorithms. This motivates the computational model of convex optimization, including the offline, online and stochastic settings, using a linear optimization oracle. In this computational model we give several new results that improve over the previous state-of-the-art. Our main result is a novel conditional gradient algorithm for smooth and strongly convex optimization over polyhedral sets that performs only a single linear optimization step over the domain on each iteration and enjoys a linear convergence rate. This gives an exponential improvement in convergence rate over previous results. #R##N#Based on this new conditional gradient algorithm we give the first algorithms for online convex optimization over polyhedral sets that perform only a single linear optimization step over the domain while having optimal regret guarantees, answering an open question of Kalai and Vempala, and Hazan and Kale. Our online algorithms also imply conditional gradient algorithms for non-smooth and stochastic convex optimization with the same convergence rates as projected (sub)gradient methods.

Connected through hyperedge HE-1 (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"
    - "projection free online learning"

Connected through hyperedge HE-2 (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"
    - "projection free online learning"
    - "strategy iteration is strongly polynomial for 2 player turn based stochastic games with a constant discount factor"

Connected through hyperedge HE-3 (induced by source paper "linear convergence of a frank wolfe type algorithm over trace norm balls"):
  with members:
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale convex minimization with a low rank constraint"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "efficient second order online learning by sketching"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "efficient frequent directions algorithm for sparse matrices"
    - "the fast convergence of incremental pca"
    - "scale free algorithms for online linear optimization"
    - "frequent directions simple and deterministic matrix sketching"
    - "faster sgd using sketched conditioning"
    - "sketching as a tool for numerical linear algebra"
    - "one pass auc optimization"

Connected through hyperedge HE-5 (induced by source paper "variance reduced and projection free stochastic optimization"):
  with members:
    - "near optimal algorithms for online matrix prediction"
    - "projection free online learning"

Connected through hyperedge HE-6 (induced by source paper "optimal stochastic strongly convex optimization with a logarithmic number of projections"):
  with members:
    - "stochastic subgradient methods with linear convergence for polyhedral convex optimization"
    - "o logt projections for stochastic optimization of smooth and strongly convex functions"
    - "projection free online learning"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "efficient stochastic gradient descent for strongly convex optimization"):
  with members:
    - "projection free online learning"
    - "making gradient descent optimal for strongly convex stochastic optimization"

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)