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 "uplink non orthogonal multiple access for uav communications"):
  Abstract: This paper investigates ground-aerial uplink non-orthogonal multiple access (NOMA) cellular networks. A rotary-wing unmanned aerial vehicle (UAV) user and multiple ground users (GUEs) are served by ground base stations (GBSs) by utilizing uplink NOMA protocol. The UAV is dispatched to upload specific information bits to each target GBSs. Specifically, our goal is to minimize the UAV mission completion time by jointly optimizing the UAV trajectory and UAV-GBS association order while taking into account the UAV's interference to non-associated GBSs. The formulated problem is a mixed integer non-convex problem and involves infinite variables. To tackle this problem, we efficiently check the feasibility of the formulated problem by utilizing graph theory and topology theory. Next, we prove that the optimal UAV trajectory need to satisfy the \emph{fly-hover-fly} structure. With this insight, we first design an efficient solution with predefined hovering locations by leveraging graph theory techniques. Furthermore, we propose an iterative UAV trajectory design by applying successive convex approximation (SCA) technique, which is guaranteed to coverage to a locally optimal solution. We demonstrate that the two proposed designs exhibit polynomial time complexity. Finally, numerical results show that: 1) the SCA based design outperforms the fly-hover-fly based design; 2) the UAV mission completion time is significantly minimized with proposed NOMA schemes compared with the orthogonal multiple access (OMA) scheme; 3) the increase of GUEs' QoS requirements will increase the UAV mission completion time.

Includes members:
  Member "interference management for cellular connected uavs a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing throughput performance in distributed mimo wi fi networks using deep reinforcement learning")
  Member "energy minimization for wireless communication with rotary wing uav":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "cellular enabled uav communication a connectivity constrained trajectory optimization perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "uav enabled communication using noma":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "constrained deep reinforcement learning for energy sustainable multi uav based random access iot networks with noma")
  Member "multiple antenna aided noma in uav networks a stochastic geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")
  Member "a tutorial on uavs for wireless networks applications challenges and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")
  Member "uplink cooperative noma for cellular connected uav":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards reliable uav swarm communication in d2d enhanced cellular network")
  Member "placement optimization of uav mounted mobile base stations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "analysis of uav communications in cell free massive mimo systems")

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)