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: "interpretable learning for self driving cars by visualizing causal attention"
  Abstract: Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.

Connected through hyperedge HE-1 (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-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 "adversarial tcav robust and effective interpretation of intermediate layers in neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "on concept based explanations in deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-4 (induced by source paper "multimodal end to end autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "towards self supervised high level sensor fusion"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "improved semantic stixels via multimodal sensor fusion"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "deep ordinal regression network for monocular depth estimation"
    - "pointpillars fast encoders for object detection from point clouds"
    - "cirl controllable imitative reinforcement learning for vision based self driving"

Connected through hyperedge HE-5 (induced by source paper "robust change captioning"):
  with members:
    - "clevr dialog a diagnostic dataset for multi round reasoning in visual dialog"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "neural baby talk"
    - "discriminability objective for training descriptive captions"
    - "learning to describe differences between pairs of similar images"

Connected through hyperedge HE-6 (induced by source paper "selectfusion a generic framework to selectively learn multisensory fusion"):
  with members:
    - "beyond tracking selecting memory and refining poses for deep visual odometry"
    - "selective sensor fusion for neural visual inertial odometry"
    - "local supports global deep camera relocalization with sequence enhancement"
    - "lo net deep real time lidar odometry"
    - "deepvio self supervised deep learning of monocular visual inertial odometry using 3d geometric constraints"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "the limits and potentials of deep learning for robotics"

Connected through hyperedge HE-7 (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"

Connected through hyperedge HE-8 (induced by source paper "visualizing the invisible occluded vehicle segmentation and recovery"):
  with members:
    - "path aggregation network for instance segmentation"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "learning to see the invisible end to end trainable amodal instance segmentation"
    - "generative image inpainting with contextual attention"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "segan segmenting and generating the invisible"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"

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.CV(Computer Vision and Pattern Recognition)