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: "search based optimal motion planning for automated driving"
  Abstract: This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.

Connected through hyperedge HE-1 (induced by source paper "overview of tools supporting planning for automated driving"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "self driving cars a survey"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "design space of behaviour planning for autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"

Connected through hyperedge HE-2 (induced by source paper "driving in dense traffic with model free reinforcement learning"):
  with members:
    - "cooperation aware lane change control in dense traffic"
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"
    - "learning agile and dynamic motor skills for legged robots"
    - "an introduction to deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "online risk bounded motion planning for autonomous vehicles in dynamic environments"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "collaborative planning for mixed autonomy lane merging"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "carla an open urban driving simulator"
    - "pomdp lite for robust robot planning under uncertainty"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"

Connected through hyperedge HE-4 (induced by source paper "jointly learnable behavior and trajectory planning for self driving vehicles"):
  with members:
    - "an auto tuning framework for autonomous vehicles"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "baidu apollo em motion planner"
    - "learning to drive in a day"
    - "virtual to real reinforcement learning for autonomous driving"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-5 (induced by source paper "a novel approach to model exploration for value function learning"):
  with members:
    - "learning implicit sampling distributions for motion planning"
    - "self normalizing neural networks"
    - "learning sampling distributions for robot motion planning"
    - "data driven planning via imitation learning"
    - "value iteration networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"):
  with members:
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "baidu apollo em motion planner"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

Connected through hyperedge HE-7 (induced by source paper "safe learning based optimal motion planning for automated driving"):
  with members:
    - "end to end learning for self driving cars"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "value iteration networks"
    - "end to end training of deep visuomotor policies"

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)