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 steering learning end to end driving model from spatial and temporal visual cues"
  Abstract: In recent years, autonomous driving algorithms using low-cost vehicle-mounted cameras have attracted increasing endeavors from both academia and industry. There are multiple fronts to these endeavors, including object detection on roads, 3-D reconstruction etc., but in this work we focus on a vision-based model that directly maps raw input images to steering angles using deep networks. This represents a nascent research topic in computer vision. The technical contributions of this work are three-fold. First, the model is learned and evaluated on real human driving videos that are time-synchronized with other vehicle sensors. This differs from many prior models trained from synthetic data in racing games. Second, state-of-the-art models, such as PilotNet, mostly predict the wheel angles independently on each video frame, which contradicts common understanding of driving as a stateful process. Instead, our proposed model strikes a combination of spatial and temporal cues, jointly investigating instantaneous monocular camera observations and vehicle's historical states. This is in practice accomplished by inserting carefully-designed recurrent units (e.g., LSTM and Conv-LSTM) at proper network layers. Third, to facilitate the interpretability of the learned model, we utilize a visual back-propagation scheme for discovering and visualizing image regions crucially influencing the final steering prediction. Our experimental study is based on about 6 hours of human driving data provided by Udacity. Comprehensive quantitative evaluations demonstrate the effectiveness and robustness of our model, even under scenarios like drastic lighting changes and abrupt turning. The comparison with other state-of-the-art models clearly reveals its superior performance in predicting the due wheel angle for a self-driving car.

Connected through hyperedge HE-1 (induced by source paper "energy based continuous inverse optimal control"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "simulating emergent properties of human driving behavior using multi agent reward augmented imitation learning"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "a survey of inverse reinforcement learning challenges methods and progress"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "simplifying reward design through divide and conquer"

Connected through hyperedge HE-2 (induced by source paper "f1 10 an open source autonomous cyber physical platform"):
  with members:
    - "decentralized mpc based obstacle avoidance for multi robot target tracking scenarios"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "learning to drive in a day"
    - "sequential circuits from regular expressions revisited"
    - "montre a tool for monitoring timed regular expressions"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "cddt fast approximate 2d ray casting for accelerated localization"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-3 (induced by source paper "comparing offline and online testing of deep neural networks an autonomous car case study"):
  with members:
    - "machine learning testing survey landscapes and horizons"
    - "paracosm a language and tool for testing autonomous driving systems"
    - "deepbillboard systematic physical world testing of autonomous driving systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "guiding deep learning system testing using surprise adequacy"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "carla an open urban driving simulator"
    - "systematic testing of convolutional neural networks for autonomous driving"

Connected through hyperedge HE-4 (induced by source paper "rethinking self driving multi task knowledge for better generalization and accident explanation ability"):
  with members:
    - "visualbackprop efficient visualization of cnns"
    - "smoothgrad removing noise by adding noise"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "carla an open urban driving simulator"
    - "axiomatic attribution for deep networks"
    - "fast scene understanding for autonomous driving"
    - "understanding convolution for semantic segmentation"
    - "end to end driving via conditional imitation learning"

Connected through hyperedge HE-5 (induced by source paper "vision based control of a quadrotor in user proximity mediated vs end to end learning approaches"):
  with members:
    - "deep drone racing learning agile flight in dynamic environments"
    - "person following by autonomous robots a categorical overview"
    - "learning to navigate autonomously in outdoor environments mavnet"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "limits of end to end learning"
    - "learning to fly by crashing"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"

Connected through hyperedge HE-6 (induced by source paper "learning to steer by mimicking features from heterogeneous auxiliary networks"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning a driving simulator"
    - "end to end learning for self driving cars"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "pyramid scene parsing network"

Connected through hyperedge HE-7 (induced by source paper "end to end multi modal multi task vehicle control for self driving cars with visual perception"):
  with members:
    - "visualbackprop efficient visualization of cnns"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "brain inspired cognitive model with attention for self driving cars"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"
    - "an empirical evaluation of deep learning on highway driving"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "end to end vehicle lateral control using a single fisheye camera"):
  with members:
    - "virtual to real reinforcement learning for autonomous driving"
    - "end to end driving via conditional imitation learning"
    - "end to end deep learning for steering autonomous vehicles considering temporal dependencies"
    - "end to end learning for self driving cars"
    - "end to end learning of driving models from large scale video datasets"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)