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: "capacity of uav enabled multicast channel joint trajectory design and power allocation"
  Abstract: This paper studies an unmanned aerial vehicle (UAV)-enabled multicast channel, in which a UAV serves as a mobile transmitter to deliver common information to a set of $K$ ground users. We aim to characterize the capacity of this channel over a finite UAV communication period, subject to its maximum speed constraint and an average transmit power constraint. To achieve the capacity, the UAV should use a sufficiently long code that spans over its whole communication period. Accordingly, the multicast channel capacity is achieved via maximizing the minimum achievable time-averaged rates of the $K$ users, by jointly optimizing the UAV's trajectory and transmit power allocation over time. However, this problem is non-convex and difficult to be solved optimally. To tackle this problem, we first consider a relaxed problem by ignoring the maximum UAV speed constraint, and obtain its globally optimal solution via the Lagrange dual method. The optimal solution reveals that the UAV should hover above a finite number of ground locations, with the optimal hovering duration and transmit power at each location. Next, based on such a multi-location-hovering solution, we present a successive hover-and-fly trajectory design and obtain the corresponding optimal transmit power allocation for the case with the maximum UAV speed constraint. Numerical results show that our proposed joint UAV trajectory and transmit power optimization significantly improves the achievable rate of the UAV-enabled multicast channel, and also greatly outperforms the conventional multicast channel with a fixed-location transmitter.

Connected through hyperedge HE-1 (induced by source paper "cognitive uav communication via joint maneuver and power control"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "cellular connected uav uplink association power control and interference coordination"
    - "optimized path planning for inspection by unmanned aerial vehicles swarm with energy constraints"
    - "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"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for unmanned aerial vehicle assisted vehicular networks"):
  with members:
    - "deep reinforcement learning for intelligent transportation systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"
    - "an online ride sharing path planning strategy for public vehicle systems"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "sgd learns the conjugate kernel class of the network"
    - "neural optimizer search with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "uav base station location optimization for next generation wireless networks overview and future research directions"):
  with members:
    - "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"
    - "backhaul aware placement of a uav bs with bandwidth allocation for user centric operation and profit maximization"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "spatial configuration of agile wireless networks with drone bss and user in the loop"
    - "efficient 3d placement of a uav using particle swarm optimization"
    - "placement optimization of uav mounted mobile base stations"
    - "providing wireless coverage to high rise buildings using uavs"

Connected through hyperedge HE-4 (induced by source paper "throughput maximization for uav enabled wireless powered communication networks"):
  with members:
    - "capacity characterization of uav enabled two user broadcast channel"
    - "placement optimization of uav mounted mobile base stations"
    - "uav enabled wireless power transfer trajectory design and energy region characterization"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "energy efficient data collection in uav enabled wireless sensor network"
    - "joint trajectory and communication design for uav enabled multiple access"
    - "uav enabled wireless power transfer trajectory design and energy optimization"
    - "joint offloading and computing optimization in wireless powered mobile edge computing systems"

Connected through hyperedge HE-5 (induced by source paper "placement optimization for uav enabled wireless networks with multi hop backhaul"):
  with members:
    - "throughput maximization for uav enabled wireless powered communication networks"
    - "uav enabled wireless power transfer trajectory design and energy region characterization"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "network formation in the sky unmanned aerial vehicles for multi hop wireless backhauling"
    - "energy efficient data collection in uav enabled wireless sensor network"
    - "uav enabled wireless power transfer trajectory design and energy optimization"
    - "joint altitude and beamwidth optimization for uav enabled multiuser communications"
    - "cyclical multiple access in uav aided communications a throughput delay tradeoff"

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)