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: "trajectory design for distributed estimation in uav enabled wireless sensor network"
  Abstract: In this paper, we study an unmanned aerial vehicle(UAV)-enabled wireless sensor network, where a UAV is dispatched to collect the sensed data from distributed sensor nodes (SNs) for estimating an unknown parameter. It is revealed that in order to minimize the mean square error (MSE) for the estimation, the UAV should collect the data from as many SNs as possible, based on which an optimization problem is formulated to design the UAV's trajectory subject to its practical mobility constraints. Although the problem is non-convex and NP-hard, we show that the optimal UAV trajectory consists of connected line segments only. With this simplification, an efficient suboptimal solution is proposed by leveraging the classic traveling salesman problem (TSP) method and applying convex optimization techniques. Simulation results show that the proposed trajectory design achieves significant performance gains in terms of the number of SNs whose data are successfully collected, as compared to other benchmark schemes.

Connected through hyperedge HE-1 (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"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "uav enabled radio access network multi mode communication and trajectory design"

Connected through hyperedge HE-2 (induced by source paper "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "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"
    - "uav aided offloading for cellular hotspot"
    - "energy minimization for wireless communication with rotary wing uav"

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)