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 deep learning on edge devices feasibility via adaptive compression"
  Abstract: A large portion of data mining and analytic services use modern machine learning techniques, such as deep learning. The state-of-the-art results by deep learning come at the price of an intensive use of computing resources. The leading frameworks (e.g., TensorFlow) are executed on GPUs or on high-end servers in datacenters. On the other end, there is a proliferation of personal devices with possibly free CPU cycles; this can enable services to run in users' homes, embedding machine learning operations. In this paper, we ask the following question: Is distributed deep learning computation on WAN connected devices feasible, in spite of the traffic caused by learning tasks? We show that such a setup rises some important challenges, most notably the ingress traffic that the servers hosting the up-to-date model have to sustain. #R##N#In order to reduce this stress, we propose adaComp, a novel algorithm for compressing worker updates to the model on the server. Applicable to stochastic gradient descent based approaches, it combines efficient gradient selection and learning rate modulation. We then experiment and measure the impact of compression, device heterogeneity and reliability on the accuracy of learned models, with an emulator platform that embeds TensorFlow into Linux containers. We report a reduction of the total amount of data sent by workers to the server by two order of magnitude (e.g., 191-fold reduction for a convolutional network on the MNIST dataset), when compared to a standard asynchronous stochastic gradient descent, while preserving model accuracy.

Connected through hyperedge HE-1 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach"):
  with members:
    - "model pruning enables efficient federated learning on edge devices"
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "advances and open problems in federated learning"
    - "introduction to online convex optimization"
    - "federated learning challenges methods and future directions"
    - "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"

Connected through hyperedge HE-2 (induced by source paper "gap aware mitigation of gradient staleness"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "making asynchronous stochastic gradient descent work for transformers"
    - "taming momentum in a distributed asynchronous environment"
    - "measuring the effects of data parallelism on neural network training"
    - "revisiting distributed synchronous sgd"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asynchronous stochastic gradient descent with delay compensation"

Connected through hyperedge HE-3 (induced by source paper "accelerating federated learning via momentum gradient descent"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated learning with non iid data"
    - "a survey on mobile edge computing the communication perspective"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized data"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "oodida on board off board distributed data analytics for connected vehicles"):
  with members:
    - "functional federated learning in erlang ffl erl"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-5 (induced by source paper "oodida on board off board distributed real time data analytics for connected vehicles"):
  with members:
    - "functional federated learning in erlang ffl erl"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-6 (induced by source paper "md gan multi discriminator generative adversarial networks for distributed datasets"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "generative adversarial parallelization"
    - "style transfer generative adversarial networks learning to play chess differently"
    - "multi generator generative adversarial nets"
    - "revisiting distributed synchronous sgd"
    - "invertible conditional gans for image editing"
    - "generative multi adversarial networks"
    - "towards geo distributed machine learning"

Connected through hyperedge HE-7 (induced by source paper "adaptive federated learning in resource constrained edge computing systems"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "federated learning with non iid data"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"

Connected through hyperedge HE-8 (induced by source paper "variance reduced stochastic learning by networked agents under random reshuffling"):
  with members:
    - "variance reduced stochastic learning under random reshuffling"
    - "federated optimization distributed machine learning for on device intelligence"
    - "communication efficient distributed dual coordinate ascent"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "on the learning behavior of adaptive networks part i transient analysis"
    - "gossip algorithms for distributed signal processing"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

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)