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: "a robust lane detection and departure warning system"
  Abstract: In this work, we have developed a robust lane detection and departure warning technique. Our system is based on single camera sensor. For lane detection a modified Inverse Perspective Mapping using only a few extrinsic camera parameters and illuminant Invariant techniques is used. Lane markings are represented using a combination of 2nd and 4th order steerable filters, robust to shadowing. Effect of shadowing and extra sun light are removed using Lab color space, and illuminant invariant representation. Lanes are assumed to be cubic curves and fitted using robust RANSAC. This method can reliably detect lanes of the road and its boundary. This method has been experimented in Indian road conditions under different challenging situations and the result obtained were very good. For lane departure angle an optical flow based method were used.

Connected through hyperedge HE-1 (induced by source paper "deep multi sensor lane detection"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "pyramid scene parsing network"
    - "an empirical evaluation of deep learning on highway driving"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "total recall understanding traffic signs using deep hierarchical convolutional neural networks"):
  with members:
    - "novel deep learning model for traffic sign detection using capsule networks"
    - "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "efficient yet deep convolutional neural networks for semantic segmentation"
    - "parsenet looking wider to see better"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "vehicle local position estimation system"

Connected through hyperedge HE-3 (induced by source paper "traffic sign classification using deep inception based convolutional networks"):
  with members:
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "vehicle local position estimation system"
    - "a novel plsa based traffic signs classification system"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "characterizing driving behavior using automatic visual analysis"

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)