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: "optimality guarantees for distributed statistical estimation"
  Abstract: Large data sets often require performing distributed statistical estimation, with a full data set split across multiple machines and limited communication between machines. To study such scenarios, we define and study some refinements of the classical minimax risk that apply to distributed settings, comparing to the performance of estimators with access to the entire data. Lower bounds on these quantities provide a precise characterization of the minimum amount of communication required to achieve the centralized minimax risk. We study two classes of distributed protocols: one in which machines send messages independently over channels without feedback, and a second allowing for interactive communication, in which a central server broadcasts the messages from a given machine to all other machines. We establish lower bounds for a variety of problems, including location estimation in several families and parameter estimation in different types of regression models. Our results include a novel class of quantitative data-processing inequalities used to characterize the effects of limited communication.

Connected through hyperedge HE-1 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data"):
  with members:
    - "mean estimation from one bit measurements"
    - "learning distributions from their samples under communication constraints"
    - "refined asymptotics for rate distortion using gaussian codebooks for arbitrary sources"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the distortion rate function of sampled wiener processes"
    - "compress and estimate in multiterminal source coding"
    - "a universal coding scheme for remote generation of continuous random variables"
    - "fundamental distortion limits of analog to digital compression"

Connected through hyperedge HE-2 (induced by source paper "limits on gradient compression 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"
    - "vqsgd vector quantized stochastic gradient descent"
    - "distributed learning with sublinear communication"
    - "finite precision stochastic optimization accounting for the bias"
    - "ratq a universal fixed length quantizer for stochastic optimization"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-3 (induced by source paper "distributed learning with dependent samples"):
  with members:
    - "realization of spatial sparseness by deep relu nets with massive data"
    - "divide and conquer local average regression"
    - "distributed learning with regularized least squares"

Connected through hyperedge HE-4 (induced by source paper "communication trade offs for synchronized distributed sgd with large step size"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication lower bounds for distributed convex optimization partition data on features"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-5 (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"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "sparsified sgd with memory"

Connected through hyperedge HE-6 (induced by source paper "distributed learning with sublinear communication"):
  with members:
    - "introduction to online convex optimization"
    - "space lower bounds for linear prediction"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "communication compression for decentralized training"
    - "detecting correlations with little memory and communication"
    - "distributed simulation and distributed inference"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-7 (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"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "mean estimation from one bit measurements"):
  with members:
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "information theoretic lower bounds on bayes risk in decentralized estimation"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs"
    - "on quantizer design for distributed bayesian estimation in sensor networks"
    - "on communication cost of distributed statistical estimation and dimensionality"
    - "exponential decay of reconstruction error from binary measurements of sparse signals"

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.IT(Information Theory)