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: "how good is sgd with random shuffling"
  Abstract: We study the performance of stochastic gradient descent (SGD) on smooth and strongly-convex finite-sum optimization problems. In contrast to the majority of existing theoretical works, which assume that individual functions are sampled with replacement, we focus here on popular but poorly-understood heuristics, which involve going over random permutations of the individual functions. This setting has been investigated in several recent works, but the optimal error rates remain unclear. In this paper, we provide lower bounds on the expected optimization error with these heuristics (using SGD with any constant step size), which elucidate their advantages and disadvantages. In particular, we prove that after $k$ passes over $n$ individual functions, if the functions are re-shuffled after every pass, the best possible optimization error for SGD is at least $\Omega\left(1/(nk)^2+1/nk^3\right)$, which partially corresponds to recently derived upper bounds. Moreover, if the functions are only shuffled once, then the lower bound increases to $\Omega(1/nk^2)$. Since there are strictly smaller upper bounds for repeated reshuffling, this proves an inherent performance gap between SGD with single shuffling and repeated shuffling. As a more minor contribution, we also provide a non-asymptotic $\Omega(1/k^2)$ lower bound (independent of $n$) for the incremental gradient method, when no random shuffling takes place. Finally, we provide an indication that our lower bounds are tight, by proving matching upper bounds for univariate quadratic functions.

Connected through hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning"):
  with members:
    - "feddane a federated newton type method"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "advances and open problems in federated learning"
    - "variance reduced local sgd with lower communication complexity"
    - "federated learning of n gram language models"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "convergence of distributed stochastic variance reduced methods without sampling extra data"

Connected through hyperedge HE-2 (induced by source paper "simple and fast algorithm for binary integer and online linear programming"):
  with members:
    - "online linear programming dual convergence new algorithms and regret bounds"
    - "introduction to online convex optimization"
    - "online convex optimization for cumulative constraints"
    - "a linearly relaxed approximate linear program for markov decision processes"
    - "fast algorithms for online stochastic convex programming"
    - "a simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
    - "trading regret for efficiency online convex optimization with long term constraints"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-3 (induced by source paper "closing the convergence gap of sgd without replacement"):
  with members:
    - "stochastic learning under random reshuffling with constant step sizes"

Connected through hyperedge HE-4 (induced by source paper "stochastic newton and cubic newton methods with simple local linear quadratic rates"):
  with members:
    - "sgd general analysis and improved rates"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "stochastic variance reduced cubic regularized newton method"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "sub sampled cubic regularization for non convex optimization"
    - "a proximal stochastic quasi newton algorithm"
    - "variance reduced second order methods"

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)