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: "path design for cellular connected uav with reinforcement learning"
  Abstract: This paper studies the path design problem for cellular-connected unmanned aerial vehicle (UAV), which aims to minimize its mission completion time while maintaining good connectivity with the cellular network. We first argue that the conventional path design approach via formulating and solving optimization problems faces several practical challenges, and then propose a new reinforcement learning-based UAV path design algorithm by applying \emph{temporal-difference} method to directly learn the \emph{state-value function} of the corresponding Markov Decision Process. The proposed algorithm is further extended by using linear function approximation with tile coding to deal with large state space. The proposed algorithms only require the raw measured or simulation-generated signal strength as the input and are suitable for both online and offline implementations. Numerical results show that the proposed path designs can successfully avoid the coverage holes of cellular networks even in the complex urban environment.

Connected through hyperedge HE-1 (induced by source paper "online maneuver design for uav enabled noma systems via reinforcement learning"):
  with members:
    - "fundamental rate limits of uav enabled multiple access channel with trajectory optimization"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "capacity characterization of uav enabled two user broadcast channel"
    - "deep learning in physical layer communications"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "mobile unmanned aerial vehicles uavs for energy efficient internet of things communications"

Connected through hyperedge HE-2 (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"
    - "age optimal trajectory planning for uav assisted data collection"
    - "local map assisted positioning for flying wireless relays"
    - "learning to communicate in uav aided wireless networks map based approaches"

Connected through hyperedge HE-3 (induced by source paper "placement optimization of aerial base stations with deep reinforcement learning"):
  with members:
    - "placement optimization of uav mounted mobile base stations"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-4 (induced by source paper "3d map assisted uav trajectory design under cellular connectivity constraints"):
  with members:
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "radio map based 3d path planning for cellular connected uav"
    - "cellular enabled uav communication a connectivity constrained trajectory optimization perspective"
    - "5g 3gpp like channel models for outdoor urban microcellular and macrocellular environments"

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.NI(Networking and Internet Architecture)