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: "learning to communicate in uav aided wireless networks map based approaches"
  Abstract: We consider a scenario where an UAV-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with (i) optimal channel parameters learning and (ii) optimal data throughput as key objectives, respectively. While the problem of throughput optimized trajectories has been addressed in prior works, the formulation of an optimized trajectory to efficiently discover the propagation parameters has not yet been addressed. When it comes to the communication phase, the advantage of this work comes from the exploitation of a 3D city map. Unfortunately, the communication trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent IoT data harvesting.

Connected through hyperedge HE-1 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception"):
  with members:
    - "efficient local map search algorithms for the placement of flying relays"
    - "secrecy transmission in large scale uav enabled wireless networks"
    - "cognitive uav communication via joint maneuver and power control"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "securing uav communications via joint trajectory and power control"

Connected through hyperedge HE-2 (induced by source paper "3d trajectory optimization in rician fading for uav enabled data harvesting"):
  with members:
    - "age optimal trajectory planning for uav assisted data collection"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "3 d placement of an unmanned aerial vehicle base station for maximum coverage of users with different qos requirements"
    - "optimal 3d trajectory design and resource allocation for solar powered uav communication systems"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "trajectory design for distributed estimation in uav enabled wireless sensor network"
    - "uav enabled radio access network multi mode communication and trajectory design"

Connected through hyperedge HE-3 (induced by source paper "hybrid offline online design for uav enabled data harvesting in probabilistic los channel"):
  with members:
    - "average peak age of information minimization in uav assisted iot networks"
    - "efficient local map search algorithms for the placement of flying relays"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "deep reinforcement learning for minimizing age of information in uav assisted networks"
    - "3d trajectory optimization in rician fading for uav enabled data harvesting"
    - "path design for cellular connected uav with reinforcement learning"
    - "age optimal trajectory planning for uav assisted data collection"
    - "local map assisted positioning for flying wireless relays"

Connected through hyperedge HE-4 (induced by source paper "radio map based 3d path planning for cellular connected uav"):
  with members:
    - "trajectory design for cellular connected uav under outage duration constraint"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "connectivity aware uav path planning with aerial coverage maps"
    - "hybrid offline online design for uav enabled data harvesting in probabilistic los channel"
    - "3d trajectory optimization in rician fading for uav enabled data harvesting"
    - "trajectory optimization for cellular connected uav under outage duration constraint"
    - "cognitive uav communication via joint maneuver and power control"
    - "comp in the sky uav placement and movement optimization for multi user communications"

Connected through hyperedge HE-5 (induced by source paper "the roadmap to 6g ai empowered wireless networks"):
  with members:
    - "6g the next frontier"
    - "a speculative study on 6g"
    - "optimizing space air ground integrated networks by artificial intelligence"
    - "a hardware efficient analog network structure for hybrid precoding in millimeter wave systems"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "data shuffling in wireless distributed computing via low rank optimization"
    - "propagation measurement system and approach at 140 ghz moving to 6g and above 100 ghz"
    - "a survey on mobile edge computing the communication perspective"

Connected through hyperedge HE-6 (induced by source paper "connectivity aware uav path planning with aerial coverage maps"):
  with members:
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "mobile networks connected drones field trials simulations and design insights"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"

Connected through hyperedge HE-7 (induced by source paper "federated echo state learning for minimizing breaks in presence in wireless virtual reality networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "computation scheduling for distributed machine learning with straggling workers"
    - "towards federated learning at scale system design"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "communication computing and caching for mobile vr delivery modeling and trade off"
    - "edge computing meets millimeter wave enabled vr paving the way to cutting the cord"
    - "analysis of memory capacity for deep echo state networks"
    - "human in the loop wireless communications machine learning and brain aware resource management"

Connected through hyperedge HE-8 (induced by source paper "environment aware deployment of wireless drones base stations with google earth simulator"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "joint trajectory and communication design for uav enabled multiple access"
    - "building detection from satellite images on a global scale"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "the new frontier in ran heterogeneity multi tier drone cells"

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)