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: "uav enabled aerial base station bs iii iii capacity characterization of uav enabled two user broadcast channel"
  Abstract: Although prior works have exploited the UAV's mobility to enhance the wireless communication performance under different setups, the fundamental capacity limits of UAV-enabled/aided multiuser communication systems have not yet been characterized. To fill this gap, we consider in this paper a UAV-enabled two-user broadcast channel (BC), where a UAV flying at a constant altitude is deployed to send independent information to two users at different fixed locations on the ground. We aim to characterize the capacity region of this new type of BC over a given UAV flight duration, by jointly optimizing the UAV's trajectory and transmit power/rate allocations over time, subject to the UAV's maximum speed and maximum transmit power constraints. First, to draw essential insights, we consider two special cases with asymptotically large/low UAV flight duration/speed, respectively. For the former case, it is shown that a simple hover-fly-hover (HFH) UAV trajectory with time division multiple access (TDMA) based orthogonal multiuser transmission is capacity-achieving, while in the latter case, the UAV should hover at a fixed location that is nearer to the user with larger achievable rate and in general superposition coding (SC) based non-orthogonal transmission with interference cancellation at the receiver of the nearer user is required. Next, we consider the general case with finite UAV speed and flight duration. We show that the optimal UAV trajectory should follow a general HFH structure, i.e., the UAV successively hovers at a pair of initial and final locations above the line segment of the two users each with a certain amount of time and flies unidirectionally between them at the maximum speed, and SC is generally needed.

Connected through hyperedge HE-1 (induced by source paper "interference management for cellular connected uavs a deep reinforcement learning approach"):
  with members:
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"
    - "reshaping cellular networks for the sky the major factors and feasibility"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "caching in the sky proactive deployment of cache enabled unmanned aerial vehicles for optimized quality of experience"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"

Connected through hyperedge HE-2 (induced by source paper "beyond 5g with uavs foundations of a 3d wireless cellular network"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "uav aided offloading for cellular hotspot"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"

Connected through hyperedge HE-3 (induced by source paper "energy optimization for cellular connected multi uav mobile edge computing systems with multi access schemes"):
  with members:
    - "fundamental tradeoffs in communication and trajectory design for uav enabled wireless network"
    - "securing uav communications via joint trajectory and power control"
    - "spectral and energy efficient wireless powered iot networks noma or tdma"
    - "energy minimization for wireless communication with rotary wing uav"
    - "mobile edge computing for cellular connected uav computation offloading and trajectory optimization"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"

Connected through hyperedge HE-4 (induced by source paper "cellular connected uav uplink association power control and interference coordination"):
  with members:
    - "fundamental tradeoffs in communication and trajectory design for uav enabled wireless network"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "cellular connected uav potentials challenges and promising technologies"
    - "mobile networks connected drones field trials simulations and design insights"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "interference mitigation methods for unmanned aerial vehicles served by cellular 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)