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: "energy efficient radio resource allocation for federated edge learning"
  Abstract: Edge machine learning involves the development of learning algorithms at the network edge to leverage massive distributed data and computation resources. Among others, the framework of federated edge learning (FEEL) is particularly promising for its data-privacy preservation. FEEL coordinates global model training at a server and local model training at edge devices over wireless links. In this work, we explore the new direction of energy-efficient radio resource management (RRM) for FEEL. To reduce devices' energy consumption, we propose energy-efficient strategies for bandwidth allocation and scheduling. They adapt to devices' channel states and computation capacities so as to reduce their sum energy consumption while warranting learning performance. In contrast with the traditional rate-maximization designs, the derived optimal policies allocate more bandwidth to those scheduled devices with weaker channels or poorer computation capacities, which are the bottlenecks of synchronized model updates in FEEL. On the other hand, the scheduling priority function derived in closed form gives preferences to devices with better channels and computation capacities. Substantial energy reduction contributed by the proposed strategies is demonstrated in learning experiments.

Connected through hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "differential privacy enabled federated learning for sensitive health data"
    - "federated learning with differential privacy algorithms and performance analysis"
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "federated learning with bayesian differential privacy"
    - "on analog gradient descent learning over multiple access fading channels"

Connected through hyperedge HE-2 (induced by source paper "energy efficient federated edge learning with joint communication and computation design"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "energy efficient federated learning over wireless communication networks"
    - "joint communication and computation optimization for wireless powered mobile edge computing with d2d offloading"
    - "an overview of data importance aware radio resource management for edge machine learning"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "robust and communication efficient federated learning from non iid data"
    - "edge ai on demand accelerating deep neural network inference via edge computing"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-3 (induced by source paper "communication efficient federated learning over multiple access channels"):
  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"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "on analog gradient descent learning over multiple access fading channels"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "federated learning via over the air computation"

Connected through hyperedge HE-4 (induced by source paper "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning"):
  with members:
    - "accelerating dnn training in wireless federated edge learning system"
    - "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-5 (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"
    - "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"
    - "a joint learning and communications framework for federated learning over wireless networks"

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"
    - "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"
    - "efficient decentralized deep learning by dynamic model averaging"

Connected through hyperedge HE-7 (induced by source paper "federated learning in the sky joint power allocation and scheduling with uav swarms"):
  with members:
    - "convergence time optimization for federated learning over wireless networks"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "wireless network intelligence at the edge"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "resource allocation for d2d communications underlaying a noma based cellular network"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-8 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "client selection for federated learning with heterogeneous resources in mobile edge"

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.IT(Information Theory)