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: "performance analysis of uplink cellular iot using different deployments of data aggregators"
  Abstract: Data aggregation is an effective solution to enable cellular support of Internet-of-things (IoT) communications. Indeed, it helps alleviate channel congestion, reduce the communication range, and extend battery-lifetime. In this paper, we use stochastic geometry to analyze the performance of uplink cellular IoT using different deployment strategies of aggregators, including terrestrial and aerial ones, e.g., drones or unmanned aerial vehicles. We focus on IoT-specific performance metrics, that are typically used by 3GPP. Specifically, we derive closed-form expressions of the average transmit power consumption, which is key to determine the lifetime of IoT devices, as well as the maximum coupling loss, which is essential to determine the maximum coverage the cellular system can support. Simulation results are presented to validate the derived theoretical expressions. It is shown that aerial aggregators can significantly extend the device lifetime and provide superior coverage compared to other deployment strategies. In addition, random deployment performs well when aggregators are densely deployed, whereas optimizing the location of a single terrestrial aggregator is beneficial when devices are more clustered.

Connected through hyperedge HE-1 (induced by source paper "energy efficient massive iot shared spectrum access over uav enabled cellular networks"):
  with members:
    - "cellular connected uav uplink association power control and interference coordination"
    - "aeronautical data aggregation and field estimation in iot networks hovering traveling time dilemma of uavs"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "on the energy distortion tradeoff in the iot"
    - "energy efficient data collection in uav enabled wireless sensor network"
    - "energy efficient uav communication with trajectory optimization"
    - "energy efficient power control a look at 5g wireless technologies"
    - "the new frontier in ran heterogeneity multi tier drone cells"

Connected through hyperedge HE-2 (induced by source paper "on the benefits of network level cooperation in iot networks with aggregators"):
  with members:
    - "iot u cellular internet of things networks over unlicensed spectrum"
    - "performance analysis of a cooperative wireless network with adaptive relays"
    - "network level cooperation in random access iot networks with aggregators"
    - "on the benefits of network level cooperation in millimeter wave communications"
    - "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"
    - "wireless network level partial relay cooperation a stable throughput analysis"
    - "massive machine type communication with data aggregation and resource scheduling"
    - "effect of energy harvesting on stable throughput in cooperative relay systems"

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)