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: "stochastic gradient push for distributed deep learning"
  Abstract: Distributed data-parallel algorithms aim to accelerate the training of deep neural networks by parallelizing the computation of large mini-batch gradient updates across multiple nodes. Approaches that synchronize nodes using exact distributed averaging (e.g., via AllReduce) are sensitive to stragglers and communication delays. The PushSum gossip algorithm is robust to these issues, but only performs approximate distributed averaging. This paper studies Stochastic Gradient Push (SGP), which combines PushSum with stochastic gradient updates. We prove that SGP converges to a stationary point of smooth, non-convex objectives at the same sub-linear rate as SGD, and that all nodes achieve consensus. We empirically validate the performance of SGP on image classification (ResNet-50, ImageNet) and machine translation (Transformer, WMT'16 En-De) workloads. Our code will be made publicly available.

Connected through hyperedge HE-1 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "sgd general analysis and improved rates"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "on the performance of exact diffusion over adaptive networks"
    - "an exact quantized decentralized gradient descent algorithm"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "variance reduced stochastic learning by networked agents under random reshuffling"

Connected through hyperedge HE-2 (induced by source paper "shadowsync performing synchronization in the background for highly scalable distributed training"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "scaling sgd batch size to 32k for imagenet training"
    - "large batch training of convolutional networks"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-3 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond"):
  with members:
    - "distributed learning in non convex environments part ii polynomial escape from saddle points"
    - "decentralized stochastic gradient tracking for empirical risk minimization"
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-4 (induced by source paper "decentralized gradient methods does topology matter"):
  with members:
    - "hop heterogeneity aware decentralized training"
    - "near optimal straggler mitigation for distributed gradient methods"
    - "flexible scheduling of distributed analytic applications"
    - "tensorflow a system for large scale machine learning"
    - "federated optimization distributed optimization beyond the datacenter"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "improving efficiency in large scale decentralized distributed training"):
  with members:
    - "distributed deep learning strategies for automatic speech recognition"
    - "lessons from building acoustic models with a million hours of speech"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"
    - "staleness aware async sgd for distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "overlap local sgd an algorithmic approach to hide communication delays in distributed sgd"):
  with members:
    - "slowmo improving communication efficient distributed sgd with slow momentum"
    - "roberta a robustly optimized bert pretraining approach"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "decentralized deep learning with arbitrary communication compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "robust and communication efficient collaborative learning"
    - "an exact quantized decentralized gradient descent algorithm"
    - "texttt deepsqueeze decentralization meets error compensated compression"

Connected through hyperedge HE-8 (induced by source paper "slowmo improving communication efficient distributed sgd with slow momentum"):
  with members:
    - "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"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "understanding the role of momentum in stochastic gradient methods"
    - "lookahead optimizer k steps forward 1 step back"
    - "roberta a robustly optimized bert pretraining approach"
    - "decentralized deep learning with arbitrary communication compression"

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)