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: "open problem the oracle complexity of convex optimization with limited memory"
  Abstract: We note that known methods achieving the optimal oracle complexity for first order convex optimization require quadratic memory, and ask whether this is necessary, and more broadly seek to characterize the minimax number of first order queries required to optimize a convex Lipschitz function subject to a memory constraint.

Connected through hyperedge HE-1 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "vqsgd vector quantized stochastic gradient descent"
    - "distributed learning with sublinear communication"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-2 (induced by source paper "finite precision stochastic optimization accounting for the bias"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "federated learning strategies for improving communication efficiency"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

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)