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 cellular connected uav under outage duration constraint"
  Abstract: In this paper, we study the trajectory design for a cellular-connected unmanned aerial vehicle (UAV) with given initial and final locations, while communicating with the ground base stations (GBSs) along its flight. We consider delay-limited communications between the UAV and its associated GBSs, where a given signal-to-noise ratio (SNR) target needs to be satisfied at the receiver. However, in practice, due to various factors such as quality-of-service (QoS) requirement, GBSs' availability and UAV mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we aim to optimize the UAV trajectory to minimize its mission completion time, subject to a constraint on the maximum tolerable outage duration in its flight. To tackle this non-convex problem, we first transform it into a more tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we then further simplify the problem and propose efficient algorithms to check the feasibility of the problem as well as to obtain its optimal and high-quality suboptimal solutions, by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform the conventional method based on dynamic programming, in terms of both performance and complexity.

Connected through hyperedge HE-1 (induced by source paper "a double q learning approach for navigation of aerial vehicles with connectivity constraint"):
  with members:
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "efficient 3d aerial base station placement considering users mobility by reinforcement learning"
    - "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-2 (induced by source paper "mobility in the sky performance and mobility analysis for cellular connected uavs"):
  with members:
    - "capacity and coverage enhancement using long endurance tethered airborne base stations"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "cellular connectivity for uavs network modeling performance analysis and design guidelines"
    - "on the 3 d placement of airborne base stations using tethered uavs"
    - "trajectory optimization for cellular connected uav under outage duration constraint"
    - "drones in distress a game theoretic countermeasure for protecting uavs against gps spoofing"
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"

Connected through hyperedge HE-3 (induced by source paper "radio map based 3d path planning for cellular connected uav"):
  with members:
    - "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"
    - "exploiting noma for multi beam uav communication in cellular uplink"

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)