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: "baidu apollo em motion planner"
  Abstract: In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. #R##N#The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. #R##N#The algorithm described in this manuscript is available at this https URL.

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 "enhanced adversarial strategically timed attacks against deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "on the use of deep autoencoders for efficient embedded reinforcement learning"
    - "adversarial objects against lidar based autonomous driving systems"
    - "when causal intervention meets adversarial examples and image masking for deep neural networks"
    - "dopamine a research framework for deep reinforcement learning"
    - "adversarial attacks and defences competition"
    - "unity a general platform for intelligent agents"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-3 (induced by source paper "autonomous last mile delivery vehicles in complex traffic environments"):
  with members:
    - "exploiting sparse semantic hd maps for self driving vehicle localization"

Connected through hyperedge HE-4 (induced by source paper "attacking vision based perception in end to end autonomous driving models"):
  with members:
    - "key ingredients of self driving cars"
    - "requirements driven test generation for autonomous vehicles with machine learning components"
    - "are self driving cars secure evasion attacks against deep neural networks for steering angle prediction"
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "towards autonomous reinforcement learning automatic setting of hyper parameters using bayesian optimization"
    - "semantic adversarial deep learning"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-5 (induced by source paper "simple physical adversarial examples against end to end autonomous driving models"):
  with members:
    - "semantic adversarial deep learning"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "robust physical world attacks on deep learning models"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-6 (induced by source paper "interaction aware kalman neural networks for trajectory prediction"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "multi agent imitation learning for driving simulation"
    - "imitating driver behavior with generative adversarial networks"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"

Connected through hyperedge HE-7 (induced by source paper "jointly learnable behavior and trajectory planning for self driving vehicles"):
  with members:
    - "an auto tuning framework for autonomous vehicles"
    - "search based optimal motion planning for automated driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "driving policy transfer via modularity and abstraction"
    - "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-8 (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"
    - "search based optimal motion planning for automated driving"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

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)