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: "spectrum sharing for internet of things a survey"
  Abstract: The Internet of Things (IoT) is a promising paradigm to accommodate massive device connections in 5G and beyond. To pave the way for future IoT, the spectrum should be planed in advance. Spectrum sharing is a preferable solution for IoT due to the scarcity of available spectrum resource. In particular, mobile operators are inclined to exploit the existing standards and infrastructures of current cellular networks and deploy IoT within licensed cellular spectrum. Yet, proprietary companies prefer to deploy IoT within unlicensed spectrum to avoid any licence fee. In this paper, we provide a survey on prevalent IoT technologies deployed within licensed cellular spectrum and unlicensed spectrum. Notably, emphasis will be on the spectrum sharing solutions including the shared spectrum, interference model, and interference management. To this end, we discuss both advantages and disadvantages of different IoT technologies. Finally, we identify challenges for future IoT and suggest potential research directions.

Connected through hyperedge HE-1 (induced by source paper "minimizing the age of information of cognitive radio based iot systems under a collision constraint"):
  with members:
    - "minimizing age of information for real time monitoring in resource constrained industrial iot networks"
    - "to skip or to switch minimizing age of information under link capacity constraint"
    - "joint status sampling and updating for minimizing age of information in the internet of things"
    - "wireless network design for control systems a survey"
    - "average age of information with hybrid arq under a resource constraint"
    - "update or wait how to keep your data fresh"
    - "the emerging internet of things marketplace from an industrial perspective a survey"

Connected through hyperedge HE-2 (induced by source paper "energy efficient uav backscatter communication with joint trajectory design and resource optimization"):
  with members:
    - "sum throughput maximization in multi tag backscattering to multiantenna reader"
    - "large intelligent surface antennas lisa making reflective radios smart"
    - "uav data collection over noma backscatter networks uav altitude and trajectory optimization"
    - "cooperative ambient backscatter communications for green internet of things"
    - "symbiotic radio a new communication paradigm for passive internet of things"
    - "energy minimization for wireless communication with rotary wing uav"
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "optimal resource allocation in full duplex ambient backscatter communication networks for wireless powered iot"

Connected through hyperedge HE-3 (induced by source paper "unlicensed spectrum sharing for massive internet of things communications"):
  with members:
    - "uncoordinated access schemes for the iot approaches regulations and performance"
    - "extending lte into the unlicensed spectrum technical analysis of the proposed variants"
    - "low power wide area networks an overview"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning based modulation and coding scheme selection in cognitive heterogeneous networks"):
  with members:
    - "cooperative ambient backscatter communications for green internet of things"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "spectrum access in cognitive radio using a two stage reinforcement learning approach"
    - "a reinforcement learning approach to proactive caching in wireless networks"
    - "deep reinforcement learning multiple access for heterogeneous wireless networks"
    - "anti jamming communications using spectrum waterfall a deep reinforcement learning approach"

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)