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: "a survey of autonomous driving common practices and emerging technologies"
  Abstract: Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

Connected through hyperedge HE-1 (induced by source paper "deep neural network perception models and robust autonomous driving systems"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "one pixel attack for fooling deep neural networks"
    - "self driving cars a survey"
    - "adversarially robust distillation"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "panoptic deeplab a simple strong and fast baseline for bottom up panoptic segmentation"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"

Connected through hyperedge HE-2 (induced by source paper "interpretable end to end urban autonomous driving with latent deep reinforcement learning"):
  with members:
    - "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "conditional affordance learning for driving in urban environments"
    - "world models"

Connected through hyperedge HE-3 (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"
    - "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"
    - "control of connected and automated vehicles state of the art and future challenges"

Connected through hyperedge HE-4 (induced by source paper "integrating deep reinforcement learning with model based path planners for automated driving"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer"
    - "simulation based reinforcement learning for real world autonomous driving"
    - "enabling pedestrian safety using computer vision techniques a case study of the 2018 uber inc self driving car crash"
    - "learning to drive in a day"
    - "carla an open urban driving simulator"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-5 (induced by source paper "an rls based instantaneous velocity estimator for extended radar tracking"):
  with members:
    - "extended object tracking introduction overview and applications"

Connected through hyperedge HE-6 (induced by source paper "nuscenes a multimodal dataset for autonomous driving"):
  with members:
    - "how much real data do we actually need analyzing object detection performance using synthetic and real data"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "starnet targeted computation for object detection in point clouds"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "d 2 city a large scale dashcam video dataset of diverse traffic scenarios"
    - "disentangling monocular 3d object detection"
    - "a baseline for 3d multi object tracking"
    - "physics based rendering for improving robustness to rain"

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)