human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient road lane marking detection with deep learning"):
  Abstract: Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time. In this paper, we propose a Lane Marking Detector (LMD) using a deep convolutional neural network to extract robust lane marking features. To improve its performance with a target of lower complexity, the dilated convolution is adopted. A shallower and thinner structure is designed to decrease the computational cost. Moreover, we also design post-processing algorithms to construct 3rd-order polynomial models to fit into the curved lanes. Our system shows promising results on the captured road scenes.

Includes members:
  Member "enet a deep neural network architecture for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "multi scale context aggregation by dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "segnet a deep convolutional encoder decoder architecture for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "learning deconvolution network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning enriched features for real image restoration and enhancement")

Question: Please tell me which class the source paper 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)