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 learning with compressed gradient differences"
  Abstract: Training large machine learning models requires a distributed computing approach, with communication of the model updates being the bottleneck. For this reason, several methods based on the compression (e.g., sparsification and/or quantization) of updates were recently proposed, including QSGD (Alistarh et al., 2017), TernGrad (Wen et al., 2017), SignSGD (Bernstein et al., 2018), and DQGD (Khirirat et al., 2018). However, none of these methods are able to learn the gradients, which renders them incapable of converging to the true optimum in the batch mode, incompatible with non-smooth regularizers, and slows down their convergence. In this work we propose a new distributed learning method --- DIANA --- which resolves these issues via compression of gradient differences. We perform a theoretical analysis in the strongly convex and nonconvex settings and show that our rates are superior to existing rates. Our analysis of block-quantization and differences between $\ell_2$ and $\ell_\infty$ quantization closes the gaps in theory and practice. Finally, by applying our analysis technique to TernGrad, we establish the first convergence rate for this method.

Connected through hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning"):
  with members:
    - "feddane a federated newton type method"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "how good is sgd with random shuffling"
    - "variance reduced local sgd with lower communication complexity"
    - "federated learning of n gram language models"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "convergence of distributed stochastic variance reduced methods without sampling extra data"

Connected through hyperedge HE-2 (induced by source paper "on biased compression for distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "scaling distributed machine learning with in network aggregation"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "advances and open problems in federated learning"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-3 (induced by source paper "gradient descent with compressed iterates"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust and communication efficient collaborative learning"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"
    - "99 of distributed optimization is a waste of time the issue and how to fix it"
    - "federated machine learning concept and applications"
    - "first analysis of local gd on heterogeneous data"
    - "client selection for federated learning with heterogeneous resources in mobile edge"

Connected through hyperedge HE-4 (induced by source paper "decentralized deep learning with arbitrary communication compression"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "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"
    - "communication compression for decentralized training"

Connected through hyperedge HE-5 (induced by source paper "natural compression for distributed deep learning"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "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"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-6 (induced by source paper "scaffold stochastic controlled averaging for on device federated learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "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"
    - "federated learning for emoji prediction in a mobile keyboard"

Connected through hyperedge HE-7 (induced by source paper "communication efficient distributed learning via lazily aggregated quantized gradients"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-8 (induced by source paper "a double residual compression algorithm for efficient distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "sgd general analysis and improved rates"
    - "communication compression for decentralized training"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "imagenet training in minutes"
    - "gradient sparsification for communication efficient distributed optimization"

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)