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: "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
  Abstract: Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough. We propose exposing the learner to synthesized data in the form of perturbations to the expert's driving, which creates interesting situations such as collisions and/or going off the road. Rather than purely imitating all data, we augment the imitation loss with additional losses that penalize undesirable events and encourage progress -- the perturbations then provide an important signal for these losses and lead to robustness of the learned model. We show that the ChauffeurNet model can handle complex situations in simulation, and present ablation experiments that emphasize the importance of each of our proposed changes and show that the model is responding to the appropriate causal factors. Finally, we demonstrate the model driving a car in the real world.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "3d point cloud processing and learning for autonomous driving"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "self driving cars a survey"
    - "governing autonomous vehicles emerging responses for safety liability privacy cybersecurity and industry risks"
    - "pixor real time 3d object detection from point clouds"
    - "deep unsupervised learning of 3d point clouds via graph topology inference and filtering"
    - "3d point cloud denoising via deep neural network based local surface estimation"
    - "fvnet 3d front view proposal generation for real time object detection from point clouds"

Connected through hyperedge HE-3 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "on the variance of the adaptive learning rate and beyond"

Connected through hyperedge HE-4 (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"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "laserflow efficient and probabilistic object detection and motion forecasting"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "learning an uncertainty aware object detector for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "yolov3 an incremental improvement"
    - "deep object centric policies for autonomous driving"
    - "recent advances in autoencoder based representation learning"
    - "advances in variational inference"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"

Connected through hyperedge HE-7 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding"):
  with members:
    - "human motion trajectory prediction a survey"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "collaborative motion prediction via neural motion message passing"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "deepgcns can gcns go as deep as cnns"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "linknet relational embedding for scene graph"

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)