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: "road crack detection using deep convolutional neural network and adaptive thresholding"
  Abstract: Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.

Connected through hyperedge HE-1 (induced by source paper "unsupervised pixel level road defect detection via adversarial image to frequency transform"):
  with members:
    - "feature pyramid and hierarchical boosting network for pavement crack detection"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "richer convolutional features for edge detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "future frame prediction for anomaly detection a new baseline"
    - "holistically nested edge detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "adaptive binarization based on fuzzy integrals"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "deepotsu document enhancement and binarization using iterative deep learning"
    - "a selectional auto encoder approach for document image binarization"
    - "deeply supervised salient object detection with short connections"
    - "semantic binary segmentation using convolutional networks without decoders"
    - "convolutional feature masking for joint object and stuff segmentation"

Connected through hyperedge HE-3 (induced by source paper "key ingredients of self driving cars"):
  with members:
    - "automatic calibration of multiple 3d lidars in urban environments"
    - "using dp towards a shortest path problem related application"
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "a novel dual lidar calibration algorithm using planar surfaces"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "real time stereo vision for road surface 3 d reconstruction"
    - "a novel disparity transformation algorithm for road 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)