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: "deep object centric policies for autonomous driving"
  Abstract: While learning visuomotor skills in an end-to-end manner is appealing, deep neural networks are often uninterpretable and fail in surprising ways. For robotics tasks, such as autonomous driving, models that explicitly represent objects may be more robust to new scenes and provide intuitive visualizations. We describe a taxonomy of "object-centric" models which leverage both object instances and end-to-end learning. In the Grand Theft Auto V simulator, we show that object-centric models outperform object-agnostic methods in scenes with other vehicles and pedestrians, even with an imperfect detector. We also demonstrate that our architectures perform well on real-world environments by evaluating on the Berkeley DeepDrive Video dataset, where an object-centric model outperforms object-agnostic models in the low-data regimes.

Connected through hyperedge HE-1 (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"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "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-2 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference"):
  with members:
    - "urban driving with conditional imitation learning"
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"
    - "causal confusion in imitation learning"
    - "grounding human to vehicle advice for self driving vehicles"
    - "goal oriented object importance estimation in on road driving videos"
    - "causal induction from visual observations for goal directed tasks"
    - "a self validation network for object level human attention estimation"

Connected through hyperedge HE-3 (induced by source paper "a survey of deep learning applications to autonomous vehicle control"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "imminent collision mitigation with reinforcement learning and vision"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "causal confusion in imitation learning"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"
    - "constrained deep neural network architecture search for iot devices accounting hardware calibration"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"

Connected through hyperedge HE-4 (induced by source paper "objects as points"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "cornernet detecting objects as paired keypoints"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-5 (induced by source paper "entity abstraction in visual model based reinforcement learning"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "monet unsupervised scene decomposition and representation"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "unsupervised learning of object keypoints for perception and control"
    - "object oriented dynamics learning through multi level abstraction"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "deep set prediction networks"
    - "unsupervised discovery of parts structure and dynamics"

Connected through hyperedge HE-6 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "object oriented dynamics predictor"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "count based exploration with the successor representation"

Connected through hyperedge HE-7 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"

Connected through hyperedge HE-8 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bop benchmark for 6d object pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"

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.AI(Artificial Intelligence)