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: "robust and communication efficient collaborative learning"
  Abstract: We consider a decentralized learning problem, where a set of computing nodes aim at solving a non-convex optimization problem collaboratively. It is well-known that decentralized optimization schemes face two major system bottlenecks: stragglers' delay and communication overhead. In this paper, we tackle these bottlenecks by proposing a novel decentralized and gradient-based optimization algorithm named as QuanTimed-DSGD. Our algorithm stands on two main ideas: (i) we impose a deadline on the local gradient computations of each node at each iteration of the algorithm, and (ii) the nodes exchange quantized versions of their local models. The first idea robustifies to straggling nodes and the second alleviates communication efficiency. The key technical contribution of our work is to prove that with non-vanishing noises for quantization and stochastic gradients, the proposed method exactly converges to the global optimal for convex loss functions, and finds a first-order stationary point in non-convex scenarios. Our numerical evaluations of the QuanTimed-DSGD on training benchmark datasets, MNIST and CIFAR-10, demonstrate speedups of up to 3x in run-time, compared to state-of-the-art decentralized optimization methods.

Connected through hyperedge HE-1 (induced by source paper "on the communication latency of wireless decentralized learning"):
  with members:
    - "update aware device scheduling for federated learning at the wireless edge"
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "scheduling policies for federated learning in wireless networks"
    - "efficient decentralized deep learning by dynamic model averaging"

Connected through hyperedge HE-2 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"):
  with members:
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "an exact quantized decentralized gradient descent algorithm"
    - "communication compression for decentralized training"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "leasgd an efficient and privacy preserving decentralized algorithm for distributed learning"
    - "gradient sparsification for communication efficient distributed optimization"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"

Connected through hyperedge HE-3 (induced by source paper "quantized push sum for gossip and decentralized optimization over directed graphs"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "an exact quantized decentralized gradient descent algorithm"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-4 (induced by source paper "fedpaq a communication efficient federated learning method with periodic averaging and quantization"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "one shot federated learning"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"
    - "an exact quantized decentralized gradient descent algorithm"

Connected through hyperedge HE-5 (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"
    - "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-6 (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"
    - "an exact quantized decentralized gradient descent algorithm"
    - "texttt deepsqueeze decentralization meets error compensated compression"
    - "communication compression for decentralized training"

Connected through hyperedge HE-7 (induced by source paper "communication efficient network distributed optimization with differential coded compressors"):
  with members:
    - "communication compression for decentralized training"
    - "quantized decentralized consensus optimization"
    - "compressed distributed gradient descent communication efficient consensus over networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "federated optimization distributed optimization beyond the datacenter"

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)