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 planning of automated vehicles in tube like road segments"
  Abstract: This paper presents a method based on linear programming for trajectory planning of automated vehicles, combining obstacle avoidance, time scheduling for the reaching of waypoints and time-optimal traversal of tube-like road segments. System modeling is conducted entirely spatial-based. Kinematic vehicle dynamics as well as time are expressed in a road-aligned coordinate frame with path along the road centerline serving as the dependent variable. We elaborate on control rate constraints in the spatial domain. A vehicle dimension constraint heuristic is proposed to constrain vehicle dimensions inside road boundaries. It is outlined how friction constraints are accounted for. The discussion is extended to dynamic vehicle models. The benefits of the proposed method are illustrated by a comparison to a time-based method.

Connected through hyperedge HE-1 (induced by source paper "a look at motion planning for autonomous vehicles at an intersection"):
  with members:
    - "distributed algorithm for collision avoidance at road intersections in the presence of communication failures"
    - "high speed trajectory planning for autonomous vehicles using a simple dynamic model"
    - "trajectory planning under vehicle dimension constraints using sequential linear programming"
    - "optimal cooperative motion planning for vehicles at intersections"

Connected through hyperedge HE-2 (induced by source paper "automating vehicles by deep reinforcement learning using task separation with hill climbing"):
  with members:
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "limits of end to end learning"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "trajectory planning under vehicle dimension constraints using sequential linear programming"
    - "brain inspired cognitive model with attention for self driving cars"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"

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)