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: "natural compression for distributed deep learning"
  Abstract: Due to their hunger for big data, modern deep learning models are trained in parallel, often in distributed environments, where communication of model updates is the bottleneck. Various update compression (e.g., quantization, sparsification, dithering) techniques have been proposed in recent years as a successful tool to alleviate this problem. In this work, we introduce a new, remarkably simple and theoretically and practically effective compression technique, which we call natural compression (NC). Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two. NC is "natural" since the nearest power of two of a real expressed as a float can be obtained without any computation, simply by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by the tiny factor 9/8 only, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to 3-4x improvement in overall theoretical running time. For applications requiring more aggressive compression, we generalize NC to natural dithering, which we prove is exponentially better than the immensely popular random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect. Finally, we show that N is particularly effective for the in-network aggregation (INA) framework for distributed training, where the update aggregation is done on a switch, which can only perform integer computations.

Connected through hyperedge HE-1 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"
    - "the hsic bottleneck deep learning without back propagation"

Connected through hyperedge HE-2 (induced by source paper "uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "federated learning challenges methods and future directions"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (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"
    - "distributed learning with compressed gradient differences"
    - "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"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-4 (induced by source paper "gradient descent with compressed iterates"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust and communication efficient collaborative 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-5 (induced by source paper "distributed fixed point methods with compressed iterates"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "sgd general analysis and improved rates"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "gradient descent with compressed iterates"
    - "federated learning strategies for improving communication efficiency"
    - "quantized decentralized consensus optimization"
    - "sparsified sgd with memory"

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)