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: "an online ride sharing path planning strategy for public vehicle systems"
  Abstract: As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of customers with minimum number of vehicles and lowest possible cost. A key component of the PV system is the online ride-sharing scheduling strategy. In this paper, we propose an efficient path planning strategy that focuses on a limited potential search area for each vehicle by filtering out the requests that violate passenger service quality level, so that the global search is reduced to local search. We analyze the performance of the proposed solution such as reduction ratio of computational complexity. Simulations based on the Manhattan taxi data set show that, the computing time is reduced by 22% compared with the exhaustive search method under the same service quality performance.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for unmanned aerial vehicle assisted vehicular networks"):
  with members:
    - "deep reinforcement learning for intelligent transportation systems"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "cellular enabled uav communication trajectory optimization under connectivity constraint"
    - "power control in uav supported ultra dense networks communications caching and energy transfer"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "sgd learns the conjugate kernel class of the network"
    - "capacity of uav enabled multicast channel joint trajectory design and power allocation"
    - "neural optimizer search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "spatial influence aware reinforcement learning for intelligent transportation system"):
  with members:
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "deep reinforcement learning for intelligent transportation systems"
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "exploiting social tie structure for cooperative wireless networking a social group utility maximization framework"
    - "continuous control with deep reinforcement learning"
    - "multiagent cooperation and competition with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "deep reinforcement learning for intelligent transportation systems"):
  with members:
    - "joint transportation and charging scheduling in public vehicle systems a game theoretic approach"
    - "adam a method for stochastic optimization"

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.AI(Artificial Intelligence)