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: "dynamic base station repositioning to improve spectral efficiency of drone small cells"
  Abstract: With recent advancements in drone technology, researchers are now considering the possibility of deploying small cells served by base stations mounted on flying drones. A major advantage of such drone small cells is that the operators can quickly provide cellular services in areas of urgent demand without having to pre-install any infrastructure. Since the base station is attached to the drone, technically it is feasible for the base station to dynamic reposition itself in response to the changing locations of users for reducing the communication distance, decreasing the probability of signal blocking, and ultimately increasing the spectral efficiency. In this paper, we first propose distributed algorithms for autonomous control of drone movements, and then model and analyse the spectral efficiency performance of a drone small cell to shed new light on the fundamental benefits of dynamic repositioning. We show that, with dynamic repositioning, the spectral efficiency of drone small cells can be increased by nearly 100\% for realistic drone speed, height, and user traffic model and without incurring any major increase in drone energy consumption.

Connected through hyperedge HE-1 (induced by source paper "recharging of flying base stations using airborne rf energy sources"):
  with members:
    - "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "wireless networks with rf energy harvesting a contemporary survey"

Connected through hyperedge HE-2 (induced by source paper "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges"):
  with members:
    - "understanding uav cellular communications from existing networks to massive mimo"
    - "supporting uav cellular communications through massive mimo"
    - "uav aided offloading for cellular hotspot"
    - "the essential guide to realizing 5g connected uavs with massive mimo"
    - "performance analysis for practical unmanned aerial vehicle networks with los nlos transmissions"
    - "promises and caveats of uplink iot ultra dense networks"
    - "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-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 "massive uav to ground communication and its stable movement control a mean field approach"):
  with members:
    - "mean field game theoretic edge caching in ultra dense networks"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "ultra dense edge caching under spatio temporal demand and network dynamics"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "revisiting frequency reuse towards supporting ultra reliable ubiquitous rate communication"
    - "energy efficient uav communication with trajectory optimization"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "user centric mobility management in ultra dense cellular networks under spatio temporal dynamics"

Connected through hyperedge HE-5 (induced by source paper "performance analysis for practical unmanned aerial vehicle networks with los nlos transmissions"):
  with members:
    - "ultra dense networks is there a limit to spatial spectrum reuse"
    - "please lower small cell antenna heights in 5g"
    - "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs"
    - "performance impact of los and nlos transmissions in dense cellular networks"
    - "drone small cells in the clouds design deployment and performance analysis"

Connected through hyperedge HE-6 (induced by source paper "coverage maximization for a poisson field of drone cells"):
  with members:
    - "key technologies and system trade offs for detection and localization of amateur drones"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "dynamic base station repositioning to improve performance of drone small cells"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "designing and implementing future aerial communication networks"
    - "a tractable approach to coverage and rate in cellular networks"

Connected through hyperedge HE-7 (induced by source paper "service on demand drone base stations cruising in the cellular network"):
  with members:
    - "dynamic base station repositioning to improve performance of drone small cells"
    - "dronecells improving 5g spectral efficiency using drone mounted flying base stations"
    - "please lower small cell antenna heights in 5g"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "towards understanding the fundamentals of mobility in cellular 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)