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: "a joint learning and communications framework for federated learning over wireless networks"
  Abstract: In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.

Connected through hyperedge HE-1 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art"):
  with members:
    - "elfish resource aware federated learning on heterogeneous edge devices"
    - "model pruning enables efficient federated learning on edge devices"
    - "one shot federated learning"
    - "privacy is what we care about experimental investigation of federated learning on edge devices"
    - "towards federated learning at scale system design"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"

Connected through hyperedge HE-2 (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"
    - "accelerating dnn training in wireless federated edge learning system"
    - "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"

Connected through hyperedge HE-3 (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"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning strategies for improving communication efficiency"
    - "broadband analog aggregation for low latency federated edge learning extended version"

Connected through hyperedge HE-4 (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"
    - "accelerating dnn training in wireless federated edge learning system"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "communication efficient federated learning over multiple access channels"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "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"
    - "federated learning via over the air computation"

Connected through hyperedge HE-6 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "on the energy and data storage management in energy harvesting wireless communications"
    - "wireless network intelligence at the edge"
    - "joint communication computation caching and control in big data multi access edge computing"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"
    - "stochastic joint radio and computational resource management for multi user mobile edge computing systems"
    - "emm energy aware mobility management for mobile edge computing in ultra dense networks"
    - "artificial neural networks based machine learning for wireless networks a tutorial"

Connected through hyperedge HE-7 (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-8 (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"
    - "revisiting distributed synchronous sgd"
    - "distributed asynchronous dual free stochastic dual coordinate ascent"
    - "adding vs averaging in distributed primal dual optimization"
    - "fast distributed asynchronous sgd with variance reduction"
    - "multi agent distributed optimization via inexact consensus admm"

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.NI(Networking and Internet Architecture)