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: "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
  Abstract: We present AutonoVi:, a novel algorithm for autonomous vehicle navigation that supports dynamic maneuvers and satisfies traffic constraints and norms. Our approach is based on optimization-based maneuver planning that supports dynamic lane-changes, swerving, and braking in all traffic scenarios and guides the vehicle to its goal position. We take into account various traffic constraints, including collision avoidance with other vehicles, pedestrians, and cyclists using control velocity obstacles. We use a data-driven approach to model the vehicle dynamics for control and collision avoidance. Furthermore, our trajectory computation algorithm takes into account traffic rules and behaviors, such as stopping at intersections and stoplights, based on an arc-spline representation. We have evaluated our algorithm in a simulated environment and tested its interactive performance in urban and highway driving scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios include jaywalking pedestrians, sudden stops from high speeds, safely passing cyclists, a vehicle suddenly swerving into the roadway, and high-density traffic where the vehicle must change lanes to progress more effectively.

Connected through hyperedge HE-1 (induced by source paper "a review of sensing and communication human factors and controller aspects for information aware connected and automated vehicles"):
  with members:
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-2 (induced by source paper "aads augmented autonomous driving simulation using data driven algorithms"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "augmented lidar simulator for autonomous driving"
    - "heter sim heterogeneous multi agent systems simulation by interactive data driven optimization"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-3 (induced by source paper "identifying driver behaviors using trajectory features for vehicle navigation"):
  with members:
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "autonomous driving among many pedestrians models and algorithms"
    - "mixedpeds pedestrian detection in unannotated videos using synthetically generated human agents for training"
    - "enabling robots to communicate their objectives"
    - "end to end learning for self driving cars"
    - "lcrowdv generating labeled videos for simulation based crowd behavior learning"

Connected through hyperedge HE-4 (induced by source paper "pedestrian dominance modeling for socially aware robot navigation"):
  with members:
    - "modeling data driven dominance traits for virtual characters using gait analysis"
    - "the socially invisible robot navigation in the social world using robot entitativity"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "enabling robots to communicate their objectives"
    - "socially aware motion planning with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"):
  with members:
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "identifying driver behaviors using trajectory features for vehicle navigation"
    - "autonomous driving among many pedestrians models and algorithms"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-6 (induced by source paper "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"):
  with members:
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "dynamic group behaviors for interactive crowd simulation"

Connected through hyperedge HE-7 (induced by source paper "model predictive control for autonomous driving based on time scaled collision cone"):
  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.RO(Robotics)