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: "communication efficient distributed sgd with sketching"
  Abstract: Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time. Motivated by the success of sketching methods in sub-linear/streaming algorithms, we introduce Sketched SGD, an algorithm for carrying out distributed SGD by communicating sketches instead of full gradients. We show that Sketched SGD has favorable convergence rates on several classes of functions. When considering all communication -- both of gradients and of updated model weights -- Sketched SGD reduces the amount of communication required compared to other gradient compression methods from $\mathcal{O}(d)$ or $\mathcal{O}(W)$ to $\mathcal{O}(\log d)$, where $d$ is the number of model parameters and $W$ is the number of workers participating in training. We run experiments on a transformer model, an LSTM, and a residual network, demonstrating up to a 40x reduction in total communication cost with no loss in final model performance. We also show experimentally that Sketched SGD scales to at least 256 workers without increasing communication cost or degrading model performance.

Connected through hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "understanding the disharmony between dropout and batch normalization by variance shift"

Connected through hyperedge HE-3 (induced by source paper "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "on the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning"
    - "understanding top k sparsification in distributed deep learning"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"
    - "sparsified sgd with memory"

Connected through hyperedge HE-4 (induced by source paper "privacy for free communication efficient learning with differential privacy using sketches"):
  with members:
    - "towards federated learning at scale system design"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "secure computation for machine learning with spdz"
    - "federated learning challenges methods and future directions"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "federated heavy hitters discovery with differential privacy"
    - "enhancing the privacy of federated learning with sketching"
    - "differentially private meta learning"

Connected through hyperedge HE-5 (induced by source paper "communication efficient and byzantine robust distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "robust federated learning in a heterogeneous environment"
    - "vqsgd vector quantized stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"

Connected through hyperedge HE-6 (induced by source paper "fast machine learning with byzantine workers and servers"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "sgd decentralized byzantine resilience"
    - "a little is enough circumventing defenses for distributed learning"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-7 (induced by source paper "vqsgd vector quantized stochastic gradient descent"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "fitting relus via sgd and quantized sgd"
    - "distributed learning with sublinear communication"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

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)