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: "distributed mean estimation with limited communication"
  Abstract: Motivated by the need for distributed learning and optimization algorithms with low communication cost, we study communication efficient algorithms for distributed mean estimation. Unlike previous works, we make no probabilistic assumptions on the data. We first show that for $d$ dimensional data with $n$ clients, a naive stochastic binary rounding approach yields a mean squared error (MSE) of $\Theta(d/n)$ and uses a constant number of bits per dimension per client. We then extend this naive algorithm in two ways: we show that applying a structured random rotation before quantization reduces the error to $\mathcal{O}((\log d)/n)$ and a better coding strategy further reduces the error to $\mathcal{O}(1/n)$ and uses a constant number of bits per dimension per client. We also show that the latter coding strategy is optimal up to a constant in the minimax sense i.e., it achieves the best MSE for a given communication cost. We finally demonstrate the practicality of our algorithms by applying them to distributed Lloyd's algorithm for k-means and power iteration for PCA.

Connected through hyperedge HE-1 (induced by source paper "rpn a residual pooling network for efficient federated learning"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "variance based gradient compression for efficient distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "signsgd compressed optimisation for non convex problems"
    - "expanding the reach of federated learning by reducing client resource requirements"
    - "sparse communication for distributed gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-2 (induced by source paper "communication efficient federated learning over multiple access channels"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "on analog gradient descent learning over multiple access fading channels"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "distributed machine learning on mobile devices a survey"):
  with members:
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "on the convergence of adam and beyond"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning of out of vocabulary words"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-5 (induced by source paper "secure federated submodel learning"):
  with members:
    - "behavior sequence transformer for e commerce recommendation in alibaba"
    - "deep leakage from gradients"
    - "towards federated learning at scale system design"
    - "making ai forget you data deletion in machine learning"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "federated machine learning concept and applications"
    - "consistent and accurate frequency oracles under local differential privacy"

Connected through hyperedge HE-6 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "distributed learning with sublinear communication"
    - "locally private learning without interaction requires separation"
    - "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "learning without interaction requires separation"

Connected through hyperedge HE-7 (induced by source paper "natural compression for distributed deep learning"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "distributed learning with compressed gradient differences"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "99 of distributed optimization is a waste of time the issue and how to fix it"
    - "federated learning strategies for improving communication efficiency"
    - "not all samples are created equal deep learning with importance sampling"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for on device federated learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "federated learning of out of vocabulary words"

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)