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: "optimizing star convex functions"
  Abstract: We introduce a polynomial time algorithm for optimizing the class of star-convex functions, under no restrictions except boundedness on a region about the origin, and Lebesgue measurability. The algorithm's performance is polynomial in the requested number of digits of accuracy, contrasting with the previous best known algorithm of Nesterov and Polyak that has exponential dependence, and that further requires Lipschitz second differentiability of the function, but has milder dependence on the dimension of the domain. Star-convex functions constitute a rich class of functions generalizing convex functions to new parameter regimes, and which confound standard variants of gradient descent; more generally, we construct a family of star-convex functions where gradient-based algorithms provably give no information about the location of the global optimum. #R##N#We introduce a new randomized algorithm for finding cutting planes based only on function evaluations, where, counterintuitively, the algorithm must look outside the feasible region to discover the structure of the star-convex function that lets it compute the next cut of the feasible region. We emphasize that the class of star-convex functions we consider is as unrestricted as possible: the class of Lebesgue measurable star-convex functions has theoretical appeal, introducing to the domain of polynomial-time algorithms a huge class with many interesting pathologies. We view our results as a step forward in understanding the scope of optimization techniques beyond the garden of convex optimization and local gradient-based methods.

Connected through hyperedge HE-1 (induced by source paper "the error feedback framework better rates for sgd with delayed gradients and compressed communication"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "sgd general analysis and improved rates"
    - "unified optimal analysis of the stochastic gradient method"
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"

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.DS(Data Structures and Algorithms)