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: "on the randomized complexity of minimizing a convex quadratic function"
  Abstract: Minimizing a convex, quadratic objective is a fundamental problem in machine learning and optimization. In this work, we study prove information-theoretic, gradient query complexity lower bounds for minimizing convex quadratic functions, which, unlike prior works, apply even for randomized algorithms. Specifically, we construct a distribution over quadratic functions that witnesses lower bounds which match those known for deterministic algorithms, up to multiplicative constants. The distribution which witnesses our lower bound is in fact quite benign: it is both closed form, and derived from classical ensembles in random matrix theory. We believe that our construction constitutes a plausible "average case" setting, and thus provides compelling evidence that the worst case and average case complexity of convex-quadratic optimization are essentially identical.

Connected through hyperedge HE-1 (induced by source paper "is local sgd better than minibatch sgd"):
  with members:
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "unified optimal analysis of the stochastic gradient method"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "the gradient complexity of linear regression"):
  with members:
    - "querying a matrix through matrix vector products"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "the noisy power method a meta algorithm with applications"

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)