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: "fast uplink grant for noma a federated learning based approach"
  Abstract: Recently, non-orthogonal multiple access (NOMA) technique have emerged and is being considered as a building block of 5G systems and beyond. In this paper, we focus on the resource allocation for NOMA-based systems and we investigate how Machine Type Devices (MTDs) can be arranged into clusters. Specifically , we propose three allocation technique to enable the integration of massive NOMA-based MTD in the 5G. Firstly, we propose a low-complexity schema where the BS assigns an MTD to a cluster based on its CSI and transmit power in order to ensure that the SIC can be performed in the uplink as well as the downlink. In the second framework, we propose a federated-learning based traffic model estimation in order to extend the capacity of the system. In fact, the BS take into account the traffic model of the MTDs in order to use time multiplexing in addition to the power multiplexing to separate MTDs. After the BS allocation, we propose a learning algorithm to allow contending MTDs synchronize their transmissions. Finally, we focus on the Quality of Service (QoS) for NOMA based devices and we propose in the third framework a QoS-aware technique, where MTDs are served differently based on their mode, i.e. regular or alarm modes. Simulation results show that the proposed techniques outperform existing technique in the literature.

Connected through hyperedge HE-1 (induced by source paper "a joint learning and communications framework for federated learning over wireless networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient radio resource allocation for federated edge learning"
    - "towards federated learning at scale system design"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "when machine learning meets big data a wireless communication perspective"
    - "multi hop federated private data augmentation with sample compression"
    - "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"

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)