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: "trajectory optimization for completion time minimization in uav enabled multicasting"
  Abstract: This paper studies an unmanned aerial vehicle (UAV)-enabled multicasting system, where a UAV is dispatched to disseminate a common file to a number of geographically distributed ground terminals (GTs). Our objective is to design the UAV trajectory to minimize its mission completion time, while ensuring that each GT is able to successfully recover the file with a high probability required. We consider the use of practical random linear network coding (RLNC) for UAV multicasting, so that each GT is able to recover the file as long as it receives a sufficiently large number of coded packets. However, the formulated UAV trajectory optimization problem is non-convex and difficult to be directly solved. To tackle this issue, we first derive an analytical lower bound for the success probability of each GT's file recovery. Based on this result, we then reformulate the problem into a more tractable form, where the UAV trajectory only needs to be designed to meet a set of constraints each on the minimum connection time with a GT, during which their distance is below a designed threshold. We show that the optimal UAV trajectory only needs to constitute connected line segments, thus it can be obtained by determining first the optimal set of waypoints and then UAV speed along the lines connecting the waypoints. We propose practical schemes for the waypoints design based on a novel concept of virtual base station (VBS) placement and by applying convex optimization techniques. Furthermore, for given set of waypoints, we obtain the optimal UAV speed over the resulting path efficiently by solving a linear programming (LP) problem. Numerical results show that the proposed UAV-enabled multicasting with optimized trajectory design achieves significant performance gains as compared to benchmark schemes.

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 "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "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"
    - "energy minimization for wireless communication with rotary wing uav"

Connected through hyperedge HE-3 (induced by source paper "capacity of uav enabled multicast channel joint trajectory design and power allocation"):
  with members:
    - "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"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "physical layer multicasting by stochastic transmit beamforming and alamouti space time coding"
    - "coordinated multicast beamforming in multicell 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)