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: "lidar based driving path generation using fully convolutional neural networks"
  Abstract: In this work, a novel learning-based approach has been developed to generate driving paths by integrating LIDAR point clouds, GPS-IMU information, and Google driving directions. The system is based on a fully convolutional neural network that jointly learns to carry out perception and path generation from real-world driving sequences and that is trained using automatically generated training examples. Several combinations of input data were tested in order to assess the performance gain provided by specific information modalities. The fully convolutional neural network trained using all the available sensors together with driving directions achieved the best MaxF score of 88.13% when considering a region of interest of 60x60 meters. By considering a smaller region of interest, the agreement between predicted paths and ground-truth increased to 92.60%. The positive results obtained in this work indicate that the proposed system may help fill the gap between low-level scene parsing and behavior-reflex approaches by generating outputs that are close to vehicle control and at the same time human-interpretable.

Connected through hyperedge HE-1 (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-2 (induced by source paper "affordance learning in direct perception for autonomous driving"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "carla an open urban driving simulator"
    - "learning from maps visual common sense for autonomous driving"
    - "end to end learning for self driving cars"
    - "places an image database for deep scene understanding"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deepdriving learning affordance for direct perception in autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "real time multi target path prediction and planning for autonomous driving aided by fcn"):
  with members:
    - "learning lightweight lane detection cnns by self attention distillation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "tiev the tongji intelligent electric vehicle in the intelligent vehicle future challenge of china"
    - "dual attention network for scene segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "sampling based algorithms for optimal motion planning using closed loop prediction"
    - "pyramid scene parsing network"

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"
    - "brain inspired cognitive model with attention for self driving cars"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-6 (induced by source paper "learning to predict ego vehicle poses for sampling based nonholonomic motion planning"):
  with members:
    - "learning human aware path planning with fully convolutional networks"
    - "universal planning networks"
    - "motion planning networks"
    - "learning sampling distributions for robot motion planning"
    - "data driven planning via imitation learning"
    - "learning from the hindsight plan episodic mpc improvement"
    - "end to end learning for self driving cars"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "mapping road lanes using laser remission and deep neural networks"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a model predictive motion planner for the iara autonomous car"
    - "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-8 (induced by source paper "lidar camera fusion for road detection using fully convolutional neural networks"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "multi scale context aggregation by dilated convolutions"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "multimodal deep learning for robust rgb d object recognition"
    - "adam a method for stochastic optimization"
    - "learning rich features from rgb d images for object detection and segmentation"

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)