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 "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"):
  Abstract: Signals from RGB and depth data carry complementary information about the scene. Conventional RGB-D semantic segmentation methods adopt two-stream fusion structure which uses two modality-specific encoders to extract features from the RGB and depth data. There is currently no explicit mechanism to model the interdependencies between the encoders. This letter proposes a novel bottom-up interactive fusion structure which introduces an interaction stream to bridge the modality-specific encoders. The interaction stream progressively aggregates modality-specific features from the encoders and computes complementary features for the encoders. To instantiate this structure, the letter proposes a residual fusion block (RFB) to formulate the interdependences of the encoders. The RFB consists of two residual units and one fusion unit with gate mechanism. It learns complementary features for the modality-specific encoders and extracts modality-specific features as well as cross-modal features. Based on the RFB, the letter presents the deep multimodal networks for RGB-D semantic segmentation called RFBNet. The experiments conducted on two datasets demonstrate the effectiveness of modeling the interdependencies and that the RFBNet outperforms state-of-the-art methods.

Includes members:
  Member "self supervised model adaptation for multimodal semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "restricted deformable convolution based road scene semantic segmentation using surround view cameras":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal semantic segmentation for fisheye urban driving images")
  Member "gff gated fully fusion for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weight mechanism adding a constant in concatenation of series connect")
  Member "3dmv joint 3d multi view prediction for 3d semantic scene segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "long term visual localization using semantically segmented images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation")
  Member "in defense of classical image processing fast depth completion on the cpu":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "confidence guided stereo 3d object detection with split depth estimation")

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)