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: "detecting traffic lights by single shot detection"
  Abstract: Recent improvements in object detection are driven by the success of convolutional neural networks (CNN). They are able to learn rich features outperforming hand-crafted features. So far, research in traffic light detection mainly focused on hand-crafted features, such as color, shape or brightness of the traffic light bulb. This paper presents a deep learning approach for accurate traffic light detection in adapting a single shot detection (SSD) approach. SSD performs object proposals creation and classification using a single CNN. The original SSD struggles in detecting very small objects, which is essential for traffic light detection. By our adaptations it is possible to detect objects much smaller than ten pixels without increasing the input image size. We present an extensive evaluation on the DriveU Traffic Light Dataset (DTLD). We reach both, high accuracy and low false positive rates. The trained model is real-time capable with ten frames per second on a Nvidia Titan Xp. Code has been made available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "distant vehicle detection using radar and vision"):
  with members:
    - "deep learning is robust to massive label noise"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "soft sampling for robust object detection"
    - "faster bounding box annotation for object detection in indoor scenes"
    - "focal loss for dense object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-2 (induced by source paper "training object detectors with noisy data"):
  with members:
    - "distant vehicle detection using radar and vision"
    - "unsupervised hard example mining from videos for improved object detection"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to reweight examples for robust deep learning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "soft sampling for robust object detection"
    - "focal loss for dense object detection"

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)