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: "vh hfcn based parking slot and lane markings segmentation on panoramic surround view"
  Abstract: The automatic parking is being massively developed by car manufacturers and providers. Until now, there are two problems with the automatic parking. First, there is no openly-available segmentation labels of parking slot on panoramic surround view (PSV) dataset. Second, how to detect parking slot and road structure robustly. Therefore, in this paper, we build up a public PSV dataset. At the same time, we proposed a highly fused convolutional network (HFCN) based segmentation method for parking slot and lane markings based on the PSV dataset. A surround-view image is made of four calibrated images captured from four fisheye cameras. We collect and label more than 4,200 surround view images for this task, which contain various illuminated scenes of different types of parking slots. A VH-HFCN network is proposed, which adopts an HFCN as the base, with an extra efficient VH-stage for better segmenting various markings. The VH-stage consists of two independent linear convolution paths with vertical and horizontal convolution kernels respectively. This modification enables the network to robustly and precisely extract linear features. We evaluated our model on the PSV dataset and the results showed outstanding performance in ground markings segmentation. Based on the segmented markings, parking slots and lanes are acquired by skeletonization, hough line transform and line arrangement.

Connected through hyperedge HE-1 (induced by source paper "detecting lane and road markings at a distance with perspective transformer layers"):
  with members:
    - "learning to cluster for proposal free instance segmentation"
    - "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping"
    - "towards end to end lane detection an instance segmentation approach"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "semantic instance segmentation with a discriminative loss function"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-2 (induced by source paper "dfnet semantic segmentation on panoramic images with dynamic loss weights and residual fusion block"):
  with members:
    - "semantic segmentation via highly fused convolutional network with multiple soft cost functions"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-3 (induced by source paper "tiev the tongji intelligent electric vehicle in the intelligent vehicle future challenge of china"):
  with members:
    - "semantic segmentation via highly fused convolutional network with multiple soft cost functions"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "the social dilemma of autonomous vehicles"
    - "yolo9000 better faster stronger"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "autonomous driving 5 years after the urban challenge the anticipatory vehicle as a cyber physical system"

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)