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 "risky action recognition in lane change video clips using deep spatiotemporal networks with segmentation mask transfer"):
  Abstract: Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipelines, limiting their availability and robustness. To address these issues, we introduce a novel deep learning based driving risk assessment framework for classifying dangerous lane change behavior in short video clips captured by a monocular camera. First, semantic segmentation masks were generated from individual video frames with a pre-trained Mask R-CNN model. Then, frames overlayed with these masks were fed into a time distributed CNN-LSTM network with a final softmax classification layer. This network was trained on a semi-naturalistic lane change dataset with annotated risk labels. A comprehensive comparison of state-of-the-art pre-trained feature extractors was carried out to find the best network layout and training strategy. The best result, with a 0.937 AUC score, was obtained with the proposed framework. Our code and trained models are available open-source1.

Includes members:
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "camera based vehicle velocity estimation from monocular video":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic slam the need for speed")
  Member "rethinking the faster r cnn architecture for temporal action localization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised temporal action localization using deep metric learning")
  Member "universal language model fine tuning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning curiosity algorithms")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "temporal 3d convnets new architecture and transfer learning for video classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "view invariant deep architecture for human action recognition using two stream motion and shape temporal dynamics")
  Member "deeptest automated testing of deep neural network driven autonomous cars":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "importance driven deep learning system testing")

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)