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 under vehicle dimension constraints using sequential linear programming"
  Abstract: This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space.

Connected through hyperedge HE-1 (induced by source paper "path planning for autonomous bus driving in urban environments"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"

Connected through hyperedge HE-2 (induced by source paper "control of connected and automated vehicles state of the art and future challenges"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "least costly energy management for series hybrid electric vehicles"
    - "fuel optimal centralized coordination of truck platooning based on shortest paths"
    - "coordinating truck platooning by clustering pairwise fuel optimal plans"
    - "computing feasible vehicle platooning opportunities for transport assignments"
    - "cooperative look ahead control for fuel efficient and safe heavy duty vehicle platooning"

Connected through hyperedge HE-3 (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 of automated vehicles in tube like road segments"
    - "optimal cooperative motion planning for vehicles at intersections"

Connected through hyperedge HE-4 (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"
    - "brain inspired cognitive model with attention for self driving cars"
    - "emergence of locomotion behaviours in rich environments"
    - "trajectory planning of automated vehicles in tube like road segments"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-5 (induced by source paper "trajectory planning of automated vehicles in tube like road segments"):
  with members:
    - "a survey of motion planning and control techniques for self driving urban vehicles"

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)