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: "accelerating dnn training in wireless federated edge learning system"
  Abstract: Training task in classical machine learning models, such as deep neural networks (DNN), is generally implemented at the remote computationally-adequate cloud center for centralized learning, which is typically time-consuming and resource-hungry. It also incurs serious privacy issue and long communication latency since massive data are transmitted to the centralized node. To overcome these shortcomings, we consider a newly-emerged framework, namely federated edge learning (FEEL), to aggregate the local learning updates at the edge server instead of users' raw data. Aiming at accelerating the training process while guaranteeing the learning accuracy, we first define a novel performance evaluation criterion, called learning efficiency and formulate a training acceleration optimization problem in the CPU scenario, where each user device is equipped with CPU. The closed-form expressions for joint batchsize selection and communication resource allocation are developed and some insightful results are also highlighted. Further, we extend our learning framework into the GPU scenario and propose a novel training function to characterize the learning property of general GPU modules. The optimal solution in this case is manifested to have the similar structure as that of the CPU scenario, recommending that our proposed algorithm is applicable in more general systems. Finally, extensive experiments validate our theoretical analysis and demonstrate that our proposal can reduce the training time and improve the learning accuracy simultaneously.

Connected through hyperedge HE-1 (induced by source paper "convergence time optimization for federated learning over wireless networks"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "towards federated learning at scale system design"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "federated neuromorphic learning of spiking neural networks for low power edge intelligence"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-2 (induced by source paper "update aware device scheduling for federated learning at the wireless edge"):
  with members:
    - "device scheduling with fast convergence for wireless federated learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "federated learning over wireless fading channels"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "age based scheduling policy for federated learning in mobile edge networks"
    - "scheduling policies for federated learning in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"

Connected through hyperedge HE-3 (induced by source paper "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "federated learning over wireless networks convergence analysis and resource allocation"
    - "client edge cloud hierarchical federated learning"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "hybrid fl cooperative learning mechanism using non iid data in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-4 (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"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "scheduling policies for federated learning in wireless networks"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-5 (induced by source paper "hierarchical federated learning across heterogeneous cellular networks"):
  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"
    - "towards federated learning at scale system design"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "robust and communication efficient federated learning from non iid data"
    - "a distributed hierarchical sgd algorithm with sparse global reduction"
    - "edge assisted hierarchical federated learning with non iid data"
    - "machine learning in the air"

Connected through hyperedge HE-6 (induced by source paper "data importance aware user scheduling for communication efficient edge machine learning"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "optimizing pipelined computation and communication for latency constrained edge learning"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "towards federated learning at scale system design"
    - "semantic effectiveness filtering and control for post 5g wireless connectivity"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "active learning solution on distributed edge computing"
    - "label less learning for traffic control in an edge network"

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)