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: "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
  Abstract: Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which are communicated to the parameter server while updated parameters are returned. Recently, distributed training with AllReduce operations gained popularity as well. While many of those operations seem appealing, little is reported about wall-clock training time improvements. In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width of two combines the best of both synchronous and asynchronous training. Specifically, for a setup consisting of a four-node GPU cluster we show wall-clock time training improvements of up to 5.4x compared to conventional approaches.

Connected through hyperedge HE-1 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "central server free federated learning over single sided trust social networks"
    - "towards federated learning at scale system design"
    - "can you really backdoor federated learning"
    - "semi cyclic stochastic gradient descent"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-2 (induced by source paper "local adaalter communication efficient stochastic gradient descent with adaptive learning rates"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"

Connected through hyperedge HE-3 (induced by source paper "heterogeneity aware asynchronous decentralized training"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "hop heterogeneity aware decentralized training"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication compression for decentralized training"
    - "beyond data and model parallelism for deep neural networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"

Connected through hyperedge HE-4 (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 sgd with sketching"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"
    - "sparsified sgd with memory"

Connected through hyperedge HE-5 (induced by source paper "central server free federated learning over single sided trust social networks"):
  with members:
    - "decentralized online learning take benefits from others data without sharing your own to track global trend"
    - "introduction to online convex optimization"
    - "federated machine learning concept and applications"
    - "asynchronous subgradient push"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "stochastic gradient push for distributed deep learning"
    - "cola decentralized linear learning"

Connected through hyperedge HE-6 (induced by source paper "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "communication compression for decentralized training"
    - "sparcml high performance sparse communication for machine learning"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "communication efficient algorithms for distributed stochastic principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "faster distributed deep net training computation and communication decoupled stochastic gradient descent"):
  with members:
    - "sparsified sgd with memory"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"
    - "revisiting distributed synchronous sgd"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-8 (induced by source paper "predicting weather uncertainty with deep convnets"):
  with members:
    - "tackling climate change with machine learning"
    - "improving strong scaling of cnn training by exploiting finer grained parallelism"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "tensorflow a system for large scale machine 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)