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: "o logt projections for stochastic optimization of smooth and strongly convex functions"
  Abstract: Traditional algorithms for stochastic optimization require projecting the solution at each iteration into a given domain to ensure its feasibility. When facing complex domains, such as positive semi-definite cones, the projection operation can be expensive, leading to a high computational cost per iteration. In this paper, we present a novel algorithm that aims to reduce the number of projections for stochastic optimization. The proposed algorithm combines the strength of several recent developments in stochastic optimization, including mini-batch, extra-gradient, and epoch gradient descent, in order to effectively explore the smoothness and strong convexity. We show, both in expectation and with a high probability, that when the objective function is both smooth and strongly convex, the proposed algorithm achieves the optimal $O(1/T)$ rate of convergence with only $O(\log T)$ projections. Our empirical study verifies the theoretical result.

Connected through hyperedge HE-1 (induced by source paper "stochastic proximal gradient descent for nuclear norm regularization"):
  with members:
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "exploiting smoothness in statistical learning sequential prediction and stochastic optimization"):
  with members:
    - "binary excess risk for smooth convex surrogates"
    - "excess risk bounds for exponentially concave losses"
    - "passive learning with target risk"
    - "recovering the optimal solution by dual random projection"
    - "hybrid deterministic stochastic methods for data fitting"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "learning from an optimization viewpoint"
    - "minimizing the misclassification error rate using a surrogate convex loss"

Connected through hyperedge HE-3 (induced by source paper "mixedgrad an o 1 t convergence rate algorithm for stochastic smooth optimization"):
  with members:
    - "recovering the optimal solution by dual random projection"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "better mini batch algorithms via accelerated gradient methods"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "optimal distributed online prediction using mini batches"

Connected through hyperedge HE-4 (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"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "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)