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: "adding vs averaging in distributed primal dual optimization"
  Abstract: Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from different machines. In this paper, we present a novel generalization of the recent communication-efficient primal-dual framework (CoCoA) for distributed optimization. Our framework, CoCoA+, allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes with convergence guarantees only allow conservative averaging. We give stronger (primal-dual) convergence rate guarantees for both CoCoA as well as our new variants, and generalize the theory for both methods to cover non-smooth convex loss functions. We provide an extensive experimental comparison that shows the markedly improved performance of CoCoA+ on several real-world distributed datasets, especially when scaling up the number of machines.

Connected through hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-2 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient federated learning over wireless communication networks"
    - "supervised learning under distributed features"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "revisiting distributed synchronous sgd"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "fast distributed asynchronous sgd with variance reduction"
    - "multi agent distributed optimization via inexact consensus admm"

Connected through hyperedge HE-3 (induced by source paper "basgd buffered asynchronous sgd for byzantine learning"):
  with members:
    - "advances and open problems in federated learning"
    - "zeno robust asynchronous sgd with arbitrary number of byzantine workers"
    - "byzantine stochastic gradient descent"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "zeno distributed stochastic gradient descent with suspicion based fault tolerance"
    - "gradient sparsification for communication efficient distributed optimization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "distributed machine learning on mobile devices a survey"):
  with members:
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "on the convergence of adam and beyond"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning of out of vocabulary words"
    - "applied federated learning improving google keyboard query suggestions"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-5 (induced by source paper "variational federated multi task learning"):
  with members:
    - "federated collaborative filtering for privacy preserving personalized recommendation system"
    - "oracle order robust adaptive continual learning"
    - "knowledge transfer in deep block modular neural networks"
    - "applied federated learning improving google keyboard query suggestions"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-6 (induced by source paper "communication trade offs for synchronized distributed sgd with large step size"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication lower bounds for distributed convex optimization partition data on features"
    - "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-7 (induced by source paper "scheduling policies for federated learning in wireless networks"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-8 (induced by source paper "central server free federated learning over single sided trust social networks"):
  with members:
    - "decentralized online learning take benefits from others data without sharing your own to track global trend"
    - "introduction to online convex optimization"
    - "federated machine learning concept and applications"
    - "asynchronous subgradient push"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "stochastic gradient push for 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)