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: "efficient decentralized deep learning by dynamic model averaging"
  Abstract: We propose an efficient protocol for decentralized training of deep neural networks from distributed data sources. The proposed protocol allows to handle different phases of model training equally well and to quickly adapt to concept drifts. This leads to a reduction of communication by an order of magnitude compared to periodically communicating state-of-the-art approaches. Moreover, we derive a communication bound that scales well with the hardness of the serialized learning problem. The reduction in communication comes at almost no cost, as the predictive performance remains virtually unchanged. Indeed, the proposed protocol retains loss bounds of periodically averaging schemes. An extensive empirical evaluation validates major improvement of the trade-off between model performance and communication which could be beneficial for numerous decentralized learning applications, such as autonomous driving, or voice recognition and image classification on mobile phones.

Connected through hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"

Connected through hyperedge HE-3 (induced by source paper "joint parameter and bandwidth allocation for improving the efficiency of partitioned edge learning"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "accelerating dnn training in wireless federated edge learning system"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "scheduling policies for federated learning in wireless networks"
    - "federated learning in mobile edge networks a comprehensive survey"

Connected through hyperedge HE-4 (induced by source paper "federated clustering via matrix factorization models from model averaging to gradient sharing"):
  with members:
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "agnostic federated learning"
    - "clustering by orthogonal nmf model and non convex penalty optimization"
    - "secure federated matrix factorization"
    - "federated machine learning concept and applications"
    - "learning from hidden traits joint factor analysis and latent clustering"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-5 (induced by source paper "secel privacy preserving verifiable and fault tolerant edge learning for autonomous vehicles"):
  with members:
    - "advances and open problems in federated learning"
    - "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
    - "federated learning for ultra reliable low latency v2v communications"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "how to backdoor federated learning"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-6 (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"
    - "robust and communication efficient collaborative learning"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "scheduling policies for federated learning in wireless networks"

Connected through hyperedge HE-7 (induced by source paper "machine learning on volatile instances"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "revisiting distributed synchronous sgd"
    - "communication efficient learning of deep networks from decentralized data"
    - "tensorflow a system for large scale machine learning"
    - "efficient straggler replication in large scale parallel computing"
    - "deep residual learning for image recognition"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-8 (induced by source paper "local sgd with periodic averaging tighter analysis and adaptive synchronization"):
  with members:
    - "recycled admm improving the privacy and accuracy of distributed algorithms"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"

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)