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: "lanenet real time lane detection networks for autonomous driving"
  Abstract: Lane detection is to detect lanes on the road and provide the accurate location and shape of each lane. It severs as one of the key techniques to enable modern assisted and autonomous driving systems. However, several unique properties of lanes challenge the detection methods. The lack of distinctive features makes lane detection algorithms tend to be confused by other objects with similar local appearance. Moreover, the inconsistent number of lanes on a road as well as diverse lane line patterns, e.g. solid, broken, single, double, merging, and splitting lines further hamper the performance. In this paper, we propose a deep neural network based method, named LaneNet, to break down the lane detection into two stages: lane edge proposal and lane line localization. Stage one uses a lane edge proposal network for pixel-wise lane edge classification, and the lane line localization network in stage two then detects lane lines based on lane edge proposals. Please note that the goal of our LaneNet is built to detect lane line only, which introduces more difficulties on suppressing the false detections on the similar lane marks on the road like arrows and characters. Despite all the difficulties, our lane detection is shown to be robust to both highway and urban road scenarios method without relying on any assumptions on the lane number or the lane line patterns. The high running speed and low computational cost endow our LaneNet the capability of being deployed on vehicle-based systems. Experiments validate that our LaneNet consistently delivers outstanding performances on real world traffic scenarios.

Connected through hyperedge HE-1 (induced by source paper "fastdraw addressing the long tail of lane detection by adapting a sequential prediction network"):
  with members:
    - "robust lane detection from continuous driving scenes using deep neural networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "towards end to end lane detection an instance segmentation approach"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "deep learning based automatic video annotation tool for self driving car"):
  with members:
    - "one click annotation with guided hierarchical object detection"
    - "seeding deep learning using wireless localization"
    - "focal loss for dense object detection"
    - "simple online and realtime tracking with a deep association metric"
    - "training object class detectors with click supervision"
    - "mot16 a benchmark for multi object tracking"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "organization of ml based product development as per iso 26262"):
  with members:
    - "self driving cars a survey"
    - "a review of deep learning with special emphasis on architectures applications and recent trends"
    - "nn dependability kit engineering neural networks for safety critical systems"
    - "runtime monitoring neuron activation patterns"
    - "active learning for deep object detection"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "a survey of safety and trustworthiness of deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-4 (induced by source paper "deep learning of subsurface flow via theory guided neural network"):
  with members:
    - "deep learning based automatic video annotation tool for self driving car"
    - "physics guided neural networks pgnn an application in lake temperature modeling"
    - "theory guided data science a new paradigm for scientific discovery from data"
    - "tensorflow a system for large scale machine learning"
    - "automatic differentiation in machine learning a survey"
    - "a primer on neural network models for natural language processing"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "vision based autonomous vehicle control using the two point visual driver control model"):
  with members:
    - "autorally an open platform for aggressive autonomous driving"
    - "vision based high speed driving with a deep dynamic observer"
    - "yolo9000 better faster stronger"
    - "fast r cnn"
    - "adam a method for stochastic optimization"

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)