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 "boosting real time driving scene parsing with shared semantics"):
  Abstract: Real-time scene parsing is a fundamental feature for autonomous driving vehicles with multiple cameras. Comparing with traditional methods which individually process the frames from each camera, in this letter we demonstrate that sharing semantics between cameras with overlapped views can boost the parsing performance. Our framework is based on a deep neural network for semantic segmentation but with two kinds of additional modules for sharing and fusing semantics. On one hand, a semantics sharing module is designed to establish the pixel-wise mapping between the input image pair. Features as well as semantics are shared by the map to reduce duplicated workload which leads to more efficient computation. On the other hand, feature fusion modules are designed to combine different modal of semantic features, which learns to leverage the information from both inputs for better results. To evaluate the effectiveness of the proposed framework, we collect a new dataset with a dual-camera vision system for driving scene parsing. Experimental results show that our network outperforms the baseline method on the parsing accuracy with comparable computations.

Includes members:
  Member "short term prediction and multi camera fusion on semantic grids":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "motion estimation in occupancy grid maps in stationary settings using recurrent neural networks")
  Member "sfnet learning object aware semantic correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "a lightweight optical flow cnn revisiting data fidelity and regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "the apolloscape open dataset for autonomous driving and its application":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles")
  Member "learning correspondence from the cycle consistency of time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "searching for mobilenetv3":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "bisenet bilateral segmentation network for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "dynamic video segmentation network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient semantic video segmentation with per frame inference")

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)