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: "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
  Abstract: In this paper, the efficient deployment and mobility of multiple unmanned aerial vehicles (UAVs), used as aerial base stations to collect data from ground Internet of Things (IoT) devices, is investigated. In particular, to enable reliable uplink communications for IoT devices with a minimum total transmit power, a novel framework is proposed for jointly optimizing the three-dimensional (3D) placement and mobility of the UAVs, device-UAV association, and uplink power control. First, given the locations of active IoT devices at each time instant, the optimal UAVs' locations and associations are determined. Next, to dynamically serve the IoT devices in a time-varying network, the optimal mobility patterns of the UAVs are analyzed. To this end, based on the activation process of the IoT devices, the time instances at which the UAVs must update their locations are derived. Moreover, the optimal 3D trajectory of each UAV is obtained in a way that the total energy used for the mobility of the UAVs is minimized while serving the IoT devices. Simulation results show that, using the proposed approach, the total transmit power of the IoT devices is reduced by 45% compared to a case in which stationary aerial base stations are deployed. In addition, the proposed approach can yield a maximum of 28% enhanced system reliability compared to the stationary case. The results also reveal an inherent tradeoff between the number of update times, the mobility of the UAVs, and the transmit power of the IoT devices. In essence, a higher number of updates can lead to lower transmit powers for the IoT devices at the cost of an increased mobility for the UAVs.

Connected through hyperedge HE-1 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"

Connected through hyperedge HE-2 (induced by source paper "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"):
  with members:
    - "hybrid offline online design for uav enabled data harvesting in probabilistic los channel"
    - "optimized trajectory design in uav based cellular networks for 3d users a double q learning approach"
    - "cognitive uav communication via joint maneuver and power control"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "energy minimization for wireless communication with rotary wing uav"
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"

Connected through hyperedge HE-3 (induced by source paper "online maneuver design for uav enabled noma systems via reinforcement learning"):
  with members:
    - "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "path design for cellular connected uav with reinforcement learning"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "deep learning in physical layer communications"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"

Connected through hyperedge HE-4 (induced by source paper "an actor critic based uav bss deploymentmethod for dynamic environments"):
  with members:
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "continuous control with deep reinforcement learning"
    - "energy efficiency evaluation of cellular networks based on spatial distributions of traffic load and power consumption"

Connected through hyperedge HE-5 (induced by source paper "noma enhanced terrestrial and aerial iot networks with partial csi"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "user association and resource allocation in unified non orthogonal multiple access enabled heterogeneous ultra dense networks"
    - "spectral and energy efficient wireless powered iot networks noma or tdma"
    - "multiple antenna aided noma in uav networks a stochastic geometry approach"
    - "multidimensional constellations for uplink scma systems a comparative study"
    - "modulation and multiple access for 5g networks"
    - "throughput of cognitive radio systems with finite blocklength codes"

Connected through hyperedge HE-6 (induced by source paper "the penetration of internet of things in robotics towards a web of robotic things"):
  with members:
    - "creating an extrovert robotic assistant via iot networking devices"
    - "a gap analysis of internet of things platforms"
    - "a new vision for smart objects and the internet of things mobile robots and long range uhf rfid sensor tags"
    - "the internet of flying things opportunities and challenges with airborne fog computing and mobile cloud in the clouds"

Connected through hyperedge HE-7 (induced by source paper "path planning for uav mounted mobile edge computing with deep reinforcement learning"):
  with members:
    - "user association and path planning for uav aided mobile edge computing with energy restriction"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "internet of things applications animal monitoring with unmanned aerial vehicle"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "uav communications for 5g and beyond recent advances and future trends"):
  with members:
    - "comparison of limited feedback schemes for noma transmission in mmwave drone networks"
    - "energy optimization for cellular connected multi uav mobile edge computing systems with multi access schemes"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "resource allocation for solar powered uav communication systems"
    - "cellular connected uav potentials challenges and promising technologies"
    - "real time optimal resource allocation for embedded uav communication systems"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "minimum throughput maximization in uav aided wireless powered communication networks"

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)