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: "towards full automated drive in urban environments a demonstration in gomentum station california"
  Abstract: Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.

Connected through hyperedge HE-1 (induced by source paper "a deep reinforcement learning based driving policy for autonomous road vehicles"):
  with members:
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "limits of end to end learning"
    - "on a formal model of safe and scalable self driving cars"
    - "brain inspired cognitive model with attention for self driving cars"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-2 (induced by source paper "long term prediction of vehicle behavior using short term uncertainty aware trajectories and high definition maps"):
  with members:
    - "human motion trajectory prediction a survey"
    - "self driving cars a survey"
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-3 (induced by source paper "deep kinematic models for physically realistic prediction of vehicle trajectories"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "microscopic traffic simulation by cooperative multi agent deep reinforcement learning"):
  with members:
    - "automated speed and lane change decision making using deep reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "elements of effective deep reinforcement learning towards tactical driving decision making"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "end to end learning for self driving cars"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "end to end tracking and semantic segmentation using recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "multimodal trajectory predictions for autonomous driving using deep convolutional networks"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "visual interaction networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-7 (induced by source paper "tiev the tongji intelligent electric vehicle in the intelligent vehicle future challenge of china"):
  with members:
    - "vh hfcn based parking slot and lane markings segmentation on panoramic surround view"
    - "semantic segmentation via highly fused convolutional network with multiple soft cost functions"
    - "the social dilemma of autonomous vehicles"
    - "yolo9000 better faster stronger"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "autonomous driving 5 years after the urban challenge the anticipatory vehicle as a cyber physical system"

Connected through hyperedge HE-8 (induced by source paper "collaborative planning for mixed autonomy lane merging"):
  with members:
    - "belief state planning for autonomously navigating urban intersections"
    - "navigating intersections with autonomous vehicles using deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"

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)