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 "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images"):
  Abstract: Semantic segmentation has made striking progress due to the success of deep convolutional neural networks. Considering the demand of autonomous driving, real-time semantic segmentation has become a research hotspot these years. However, few real-time RGB-D fusion semantic segmentation studies are carried out despite readily accessible depth information nowadays. In this paper, we propose a real-time fusion semantic segmentation network termed RFNet that efficiently exploits complementary features from depth information to enhance the performance in an attention-augmented way, while running swiftly that is a necessity for autonomous vehicles applications. Multi-dataset training is leveraged to incorporate unexpected small obstacle detection, enriching the recognizable classes required to face unforeseen hazards in the real world. A comprehensive set of experiments demonstrates the effectiveness of our framework. On \textit{Cityscapes}, Our method outperforms previous state-of-the-art semantic segmenters, with excellent accuracy and 22Hz inference speed at the full 2048$\times$1024 resolution, outperforming most existing RGB-D networks.

Includes members:
  Member "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "acnet attention based network to exploit complementary features for rgbd semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hard pixel mining for depth privileged semantic segmentation")
  Member "see clearer at night towards robust nighttime semantic segmentation through day night image conversion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images")
  Member "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation")
  Member "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep neural network perception models and robust autonomous driving systems")
  Member "mergenet a deep net architecture for small obstacle discovery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lidar guided small obstacle segmentation")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "rednet residual encoder decoder network for indoor rgb d semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quadtree generating networks efficient hierarchical scene parsing with sparse convolutions")

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)