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: "beyond 5g with uavs foundations of a 3d wireless cellular network"
  Abstract: In this paper, a novel concept of three-dimensional (3D) cellular networks, that integrate drone base stations (drone-BS) and cellular-connected drone users (drone-UEs), is introduced. For this new 3D cellular architecture, a novel framework for network planning for drone-BSs as well as latency-minimal cell association for drone-UEs is proposed. For network planning, a tractable method for drone-BSs' deployment based on the notion of truncated octahedron shapes is proposed that ensures full coverage for a given space with minimum number of drone-BSs. In addition, to characterize frequency planning in such 3D wireless networks, an analytical expression for the feasible integer frequency reuse factors is derived. Subsequently, an optimal 3D cell association scheme is developed for which the drone-UEs' latency, considering transmission, computation, and backhaul delays, is minimized. To this end, first, the spatial distribution of the drone-UEs is estimated using a kernel density estimation method, and the parameters of the estimator are obtained using a cross-validation method. Then, according to the spatial distribution of drone-UEs and the locations of drone-BSs, the latency-minimal 3D cell association for drone-UEs is derived by exploiting tools from optimal transport theory. Simulation results show that the proposed approach reduces the latency of drone-UEs compared to the classical cell association approach that uses a signal-to-interference-plus-noise ratio (SINR) criterion. In particular, the proposed approach yields a reduction of up to 46% in the average latency compared to the SINR-based association. The results also show that the proposed latency-optimal cell association improves the spectral efficiency of a 3D wireless cellular network of drones.

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"
    - "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"
    - "learning to communicate in uav aided wireless networks map based approaches"

Connected through hyperedge HE-2 (induced by source paper "on the age of information in internet of things systems with correlated devices"):
  with members:
    - "age of information analysis for dynamic spectrum sharing"
    - "minimizing the age of information of cognitive radio based iot systems under a collision constraint"
    - "sampling for remote estimation through queues age of information and beyond"
    - "a reinforcement learning framework for optimizing age of information in rf powered communication systems"
    - "age of information in multiple sensing of a single source"
    - "status from a random field how densely should one update"
    - "age of information in multiple sensing"
    - "minimizing the age of information in a csma environment"

Connected through hyperedge HE-3 (induced by source paper "air to air communications beyond 5g a novel 3d comp transmission scheme"):
  with members:
    - "coordinated multi point transmission a poisson delaunay triangulation based approach"
    - "a tractable framework for coverage analysis of cellular connected uav networks"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "supporting uav cellular communications through massive mimo"
    - "unified analytical volume distribution of poisson delaunay simplex and its application to coordinated multi point transmission"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "distributed federated learning for ultra reliable low latency vehicular communications"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning for ultra reliable low latency v2v communications"
    - "federated learning strategies for improving communication efficiency"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "joint status sampling and updating for minimizing age of information in the internet of things"

Connected through hyperedge HE-5 (induced by source paper "federated learning in the sky joint power allocation and scheduling with uav swarms"):
  with members:
    - "convergence time optimization for federated learning over wireless networks"
    - "energy efficient radio resource allocation for federated edge learning"
    - "scheduling policies for federated learning in wireless networks"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "wireless network intelligence at the edge"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "resource allocation for d2d communications underlaying a noma based cellular network"
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-6 (induced by source paper "noma enhanced terrestrial and aerial iot networks with partial csi"):
  with members:
    - "user association and resource allocation in unified non orthogonal multiple access enabled heterogeneous ultra dense networks"
    - "spectral and energy efficient wireless powered iot networks noma or tdma"
    - "multiple antenna aided noma in uav networks a stochastic geometry approach"
    - "multidimensional constellations for uplink scma systems a comparative study"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"
    - "modulation and multiple access for 5g networks"
    - "throughput of cognitive radio systems with finite blocklength codes"

Connected through hyperedge HE-7 (induced by source paper "outage probability minimization for uav enabled data collection with distributed beamforming"):
  with members:
    - "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"
    - "common throughput maximization for uav enabled interference channel with wireless powered communications"
    - "3d trajectory optimization in rician fading for uav enabled data harvesting"
    - "energy efficient data collection in uav enabled wireless sensor network"
    - "uav enabled wireless power transfer trajectory design and energy optimization"

Connected through hyperedge HE-8 (induced by source paper "dynamic radar network of uavs a joint navigation and tracking approach"):
  with members:
    - "design challenges of multi uav systems in cyber physical applications a comprehensive survey and future directions"
    - "a scalable algorithm for tracking an unknown number of targets using multiple sensors"

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)