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 sublinear communication"
  Abstract: In distributed statistical learning, $N$ samples are split across $m$ machines and a learner wishes to use minimal communication to learn as well as if the examples were on a single machine. This model has received substantial interest in machine learning due to its scalability and potential for parallel speedup. However, in high-dimensional settings, where the number examples is smaller than the number of features ("dimension"), the speedup afforded by distributed learning may be overshadowed by the cost of communicating a single example. This paper investigates the following question: When is it possible to learn a $d$-dimensional model in the distributed setting with total communication sublinear in $d$? #R##N#Starting with a negative result, we show that for learning $\ell_1$-bounded or sparse linear models, no algorithm can obtain optimal error until communication is linear in dimension. Our main result is that that by slightly relaxing the standard boundedness assumptions for linear models, we can obtain distributed algorithms that enjoy optimal error with communication logarithmic in dimension. This result is based on a family of algorithms that combine mirror descent with randomized sparsification/quantization of iterates, and extends to the general stochastic convex optimization model.

Connected through hyperedge HE-1 (induced by source paper "limits on gradient compression for stochastic optimization"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "vqsgd vector quantized stochastic gradient descent"
    - "finite precision stochastic optimization accounting for the bias"
    - "ratq a universal fixed length quantizer for stochastic optimization"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "moniqua modulo quantized communication in decentralized sgd"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "asynchronous decentralized optimization in directed networks"
    - "texttt deepsqueeze decentralization meets error compensated compression"
    - "communication compression for decentralized training"
    - "high accuracy low precision training"
    - "quantized decentralized consensus optimization"
    - "distributed learning over unreliable networks"
    - "sparsified sgd with memory"

Connected through hyperedge HE-3 (induced by source paper "interaction is necessary for distributed learning with privacy or communication constraints"):
  with members:
    - "inference under information constraints ii communication constraints and shared randomness"
    - "locally private learning without interaction requires separation"
    - "estimating smooth glm in non interactive local differential privacy model with public unlabeled data"
    - "exponential separations in local differential privacy through communication complexity"
    - "the role of interactivity in local differential privacy"
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "learning without interaction requires separation"
    - "hadamard response estimating distributions privately efficiently and with little communication"

Connected through hyperedge HE-4 (induced by source paper "ratq a universal fixed length quantizer for stochastic optimization"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "vqsgd vector quantized stochastic gradient descent"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "sparsified sgd with memory"
    - "inference under information constraints i lower bounds from chi square contraction"

Connected through hyperedge HE-5 (induced by source paper "hyper sphere quantization communication efficient sgd for federated learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "robust and communication efficient federated learning from non iid data"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"
    - "sparsified sgd with memory"
    - "stochastic gradient push for distributed deep learning"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-6 (induced by source paper "finite precision stochastic optimization accounting for the bias"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "inference under information constraints ii communication constraints and shared randomness"
    - "open problem the oracle complexity of convex optimization with limited memory"
    - "geometric lower bounds for distributed parameter estimation under communication constraints"
    - "federated learning strategies for improving communication efficiency"
    - "inference under information constraints i lower bounds from chi square contraction"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"

Connected through hyperedge HE-7 (induced by source paper "vqsgd vector quantized stochastic gradient descent"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "fitting relus via sgd and quantized sgd"
    - "communication efficient distributed sgd with sketching"
    - "federated learning strategies for improving communication efficiency"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "terngrad ternary gradients to reduce communication in distributed deep 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)