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: "low power wide area networks for sustainable iot"
  Abstract: Low-power wide-area (LPWA) networks are attracting extensive attention because of their abilities to offer low-cost and massive connectivity to Internet of Things (IoT) devices distributed over wide geographical areas. This article provides a brief overview on the existing LPWA technologies and useful insights to aid the large-scale deployment of LPWA networks. Particularly, we first review the currently competing candidates of LPWA networks, such as narrowband IoT (NB-IoT) and long range (LoRa), in terms of technical fundamentals and large-scale deployment potential. Then we present two implementation examples on LPWA networks. By analyzing the field-test results, we identify several challenges that prevent LPWA technologies moving from the theory to wide-spread practice.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of clustered lora networks"):
  with members:
    - "deep learning in physical layer communications"
    - "low power wide area network analysis can lora scale"
    - "mitigating inter network interference in lora networks"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"
    - "enhancing the physical layer security of non orthogonal multiple access in large scale networks"
    - "modeling and performance analysis of clustered device to device networks"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-2 (induced by source paper "resource allocation in wireless powered iot networks"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"
    - "online learning schemes for power allocation in energy harvesting communications"
    - "wireless powered communication networks an overview"
    - "wireless networks with rf energy harvesting a contemporary survey"
    - "throughput maximization in wireless powered communication networks"
    - "multi antenna wireless powered communication with energy beamforming"
    - "peer effects and stability in matching markets"

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)