human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep reinforcement learning for unmanned aerial vehicle assisted vehicular networks"):
  Abstract: Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight links, and flexible mobility. In this paper, we study a UAV-assisted vehicular network where the UAV jointly adjusts its transmission control (power and channel) and 3D flight to maximize the total throughput. First, we formulate a Markov decision process (MDP) problem by modeling the mobility of the UAV/vehicles and the state transitions. Secondly, we solve the target problem using a deep reinforcement learning method, namely, the deep deterministic policy gradient (DDPG), and propose three solutions with different control objectives. Considering the energy consumption of 3D flight, we extend the proposed solutions to maximize the total throughput per energy unit by encouraging or discouraging the UAV's mobility. To achieve this goal, the DDPG framework is modified. Thirdly, in a simplified model with small state space and action space, we verify the optimality of proposed algorithms. Comparing with two baseline schemes, we demonstrate the effectiveness of proposed algorithms in a realistic model.

Includes members:
  Member "deep reinforcement learning for intelligent transportation systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system")
  Member "deep reinforcement learning for dynamic multichannel access in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "cellular enabled uav communication trajectory optimization under connectivity constraint":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interference management for cellular connected uavs a deep reinforcement learning approach")
  Member "an online ride sharing path planning strategy for public vehicle systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system")
  Member "power control in uav supported ultra dense networks communications caching and energy transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "throughput maximization for uav aided backscatter communication networks")
  Member "joint transportation and charging scheduling in public vehicle systems a game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coupled charging and driving incentives design for electric vehicles in urban networks")
  Member "sgd learns the conjugate kernel class of the network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "capacity of uav enabled multicast channel joint trajectory design and power allocation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cognitive uav communication via joint maneuver and power control")

Question: Please tell me which class the source paper 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.LG(Machine Learning)