human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "common throughput maximization in uav enabled ofdma systems with delay consideration"):
  Abstract: The use of unmanned aerial vehicles (UAVs) as communication platforms is of great practical significance in future wireless networks, especially for on-demand deployment in temporary events and emergency situations. Although prior works have shown the performance improvement by exploiting the UAV's mobility, they mainly focus on delay-tolerant applications. As delay requirements fundamentally limit the UAV's mobility, it remains unknown whether the UAV is able to provide any performance gain in delay-constrained communication scenarios. Motivated by this, we study in this paper a UAV-enabled orthogonal frequency division multiple access (OFDMA) network where a UAV is dispatched as a mobile base station (BS) to serve a group of users on the ground. We consider a minimum-rate ratio (MRR) for each user, defined as the minimum instantaneous rate required over the average achievable throughput, to flexibly adjust the percentage of its delay-constrained data traffic. Under a given set of constraints on the users' MRRs, we aim to maximize the minimum average throughput of all users by jointly optimizing the UAV trajectory and OFDMA resource allocation. First, we show that the max-min throughput in general decreases as the users' MRR constraints become more stringent, which reveals a fundamental throughput-delay tradeoff in UAV-enabled communications. Next, we propose an iterative parameter-assisted block coordinate descent method to optimize the UAV trajectory and OFDMA resource allocation alternately, by applying the successive convex optimization and the Lagrange duality, respectively. Furthermore, an efficient and systematic UAV trajectory initialization scheme is proposed based on a simple circular trajectory. Finally, simulation results are provided to verify our theoretical findings and demonstrate the effectiveness of our proposed designs.

Includes members:
  Member "capacity characterization of uav enabled two user broadcast channel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamental rate limits of uav enabled multiple access channel with trajectory optimization")
  Member "the sky is not the limit lte for unmanned aerial vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "wireless powered cooperative jamming for secure ofdm system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "securing uav communications via joint trajectory and power control")
  Member "dynamic base station repositioning to improve performance of drone small cells":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "power constrained trajectory optimization for wireless uav relays with random requests")
  Member "placement optimization of uav mounted mobile base stations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "joint trajectory and communication design for multi uav enabled wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "securing uav communications via trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "resource allocation for secure multi uav communication systems with multi eavesdropper")
  Member "fundamental green tradeoffs progresses challenges and impacts on 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beamforming optimization for intelligent reflecting surface assisted mimo a sum path gain maximization approach")

Question: Please tell me which class the source paper 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)