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: "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
  Abstract: This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality of service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial BS assisted one, we keep the total number of BSs similar in both networks. Obtaining the max performance in such networks highly depends on the optimal ultimate placement of the aerial-BS. To this end, we need an algorithm which can rely on more general and realistic assumptions and can decide where to go based on the past experiences. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.

Connected through hyperedge HE-1 (induced by source paper "voting based multi agent reinforcement learning for intelligent iot"):
  with members:
    - "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "scalable bilinear pi learning using state and action features"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "placement optimization of uav mounted mobile base stations"

Connected through hyperedge HE-2 (induced by source paper "distributed collaborative 3d deployment of uav base stations for on demand coverage"):
  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"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "the new frontier in ran heterogeneity multi tier drone cells"
    - "drone small cells in the clouds design deployment and performance analysis"

Connected through hyperedge HE-3 (induced by source paper "a double q learning approach for navigation of aerial vehicles with connectivity constraint"):
  with members:
    - "trajectory design for cellular connected uav under outage duration constraint"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "the new frontier in ran heterogeneity multi tier drone cells"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "q learning based aerial base station placement for fairness enhancement in mobile networks"):
  with members:
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "joint altitude beamwidth location and bandwidth optimization for uav enabled communications"
    - "placement optimization of uav mounted mobile base stations"
    - "optimal transport theory for cell association in uav enabled cellular networks"
    - "coexistence of terrestrial and aerial users in cellular networks"
    - "backhaul aware robust 3d drone placement in 5g wireless networks"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "a quantitative measure of fairness and discrimination for resource allocation in shared computer systems"

Connected through hyperedge HE-5 (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"
    - "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"
    - "joint trajectory and communication design for multi uav enabled wireless networks"

Connected through hyperedge HE-6 (induced by source paper "on demand deployment of multiple aerial base stations for traffic offloading and network recovery"):
  with members:
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "placement optimization of uav mounted mobile base stations"
    - "coulomb gans provably optimal nash equilibria via potential fields"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "drone small cells in the clouds design deployment and performance analysis"

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.NI(Networking and Internet Architecture)