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: "polling systems based autonomous vehicle coordination in traffic intersections with no traffic signals"
  Abstract: The rapid development of autonomous vehicles spurred a careful investigation of the potential benefits of all-autonomous transportation networks. Most studies conclude that autonomous systems can enable drastic improvements in performance. A widely studied concept is all-autonomous, collision-free intersections, where vehicles arriving in a traffic intersection with no traffic light adjust their speeds to cross safely through the intersection as quickly as possible. In this paper, we propose a coordination control algorithm for this problem, assuming stochastic models for the arrival times of the vehicles. The proposed algorithm provides provable guarantees on safety and performance. More precisely, it is shown that no collisions occur surely, and moreover a rigorous upper bound is provided for the expected wait time. The algorithm is also demonstrated in simulations. The proposed algorithms are inspired by polling systems. In fact, the problem studied in this paper leads to a new polling system where customers are subject to differential constraints, which may be interesting in its own right.

Connected through hyperedge HE-1 (induced by source paper "safe and efficient intersection control of connected and autonomous intersection traffic"):
  with members:
    - "autonomous and connected intersection crossing traffic management using discrete time occupancies trajectory"
    - "power traffic coordinated operation for bi peak shaving and bi ramp smoothing a hierarchical data driven approach"
    - "chance constrained day ahead hourly scheduling in distribution system operation"
    - "load forecasting based distribution system network reconfiguration a distributed data driven approach"
    - "a market inspired approach for intersection management in urban road traffic networks"

Connected through hyperedge HE-2 (induced by source paper "multi vehicle trajectory optimisation on road networks"):
  with members:
    - "overview a hierarchical framework for plan generation and execution in multirobot systems"
    - "downwash aware trajectory planning for large quadrotor teams"
    - "multi vehicle cooperative control using mixed integer linear programming"

Connected through hyperedge HE-3 (induced by source paper "flow a modular learning framework for autonomy in traffic"):
  with members:
    - "dissipation of stop and go waves via control of autonomous vehicles field experiments"
    - "textworld a learning environment for text based games"
    - "simulation to scaled city zero shot policy transfer for traffic control via autonomous vehicles"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "rllib abstractions for distributed reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "end to end learning for self driving cars"
    - "torchcraft a library for machine learning research on real time strategy games"

Connected through hyperedge HE-4 (induced by source paper "autonomous and connected intersection crossing traffic management using discrete time occupancies trajectory"):
  with members:
    - "action recognition based on joint trajectory maps using convolutional neural networks"
    - "a market inspired approach for intersection management in urban road traffic networks"

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.SY(Systems and Control)