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: "brain inspired cognitive model with attention for self driving cars"
  Abstract: Perception-driven approach and end-to-end system are two major vision-based frameworks for self-driving cars. However, it is difficult to introduce attention and historical information of autonomous driving process, which are the essential factors for achieving human-like driving into these two methods. In this paper, we propose a novel model for self-driving cars named brain-inspired cognitive model with attention (CMA). This model consists of three parts: a convolutional neural network for simulating human visual cortex, a cognitive map built to describe relationships between objects in complex traffic scene and a recurrent neural network that combines with the real-time updated cognitive map to implement attention mechanism and long-short term memory. The benefit of our model is that can accurately solve three tasks simultaneously:1) detection of the free space and boundaries of the current and adjacent lanes. 2)estimation of obstacle distance and vehicle attitude, and 3) learning of driving behavior and decision making from human driver. More significantly, the proposed model could accept external navigating instructions during an end-to-end driving process. For evaluation, we build a large-scale road-vehicle dataset which contains more than forty thousand labeled road images captured by three cameras on our self-driving car. Moreover, human driving activities and vehicle states are recorded in the meanwhile.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "end to end learning of driving models from large scale video datasets"

Connected through hyperedge HE-3 (induced by source paper "amused a multi stream vector representation method for use in natural dialogue"):
  with members:
    - "cesi canonicalizing open knowledge bases using embeddings and side information"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "improving distantly supervised relation extraction using word and entity based attention"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "reside improving distantly supervised neural relation extraction using side information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "complex sequential question answering towards learning to converse over linked question answer pairs with a knowledge graph"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-4 (induced by source paper "end to end learning of driving models with surround view cameras and route planners"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dark model adaptation semantic image segmentation from daytime to nighttime"
    - "event based vision meets deep learning on steering prediction for self driving cars"
    - "failure prediction for autonomous driving"
    - "automated map reading image based localisation in 2 d maps using binary semantic descriptors"
    - "computer vision for autonomous vehicles problems datasets and state of the art"

Connected through hyperedge HE-5 (induced by source paper "failure prediction for autonomous driving"):
  with members:
    - "semantic foggy scene understanding with synthetic data"
    - "learning driving models with a surround view camera system and a route planner"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "predicting deeper into the future of semantic segmentation"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (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"
    - "deep steering learning end to end driving model from spatial and temporal visual cues"
    - "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-7 (induced by source paper "automating vehicles by deep reinforcement learning using task separation with hill climbing"):
  with members:
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "limits of end to end learning"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "trajectory planning under vehicle dimension constraints using sequential linear programming"
    - "emergence of locomotion behaviours in rich environments"
    - "trajectory planning of automated vehicles in tube like road segments"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-8 (induced by source paper "interpretable learning for self driving cars by visualizing causal attention"):
  with members:
    - "graying the black box understanding dqns"
    - "end to end learning for self driving cars"
    - "generating visual explanations"
    - "end to end learning of driving models from large scale video datasets"
    - "densecap fully convolutional localization networks for dense captioning"
    - "deep residual learning for image recognition"
    - "action recognition using visual attention"
    - "deepdriving learning affordance for direct perception in 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.CV(Computer Vision and Pattern Recognition)