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: "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.

Connected through hyperedge HE-1 (induced by source paper "real time fusion network for rgb d semantic segmentation incorporating unexpected obstacle detection for road driving images"):
  with members:
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "acnet attention based network to exploit complementary features for rgbd semantic segmentation"
    - "see clearer at night towards robust nighttime semantic segmentation through day night image conversion"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"
    - "mergenet a deep net architecture for small obstacle discovery"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "squeeze and excitation networks"

Connected through hyperedge HE-2 (induced by source paper "uno uncertainty aware noisy or multimodal fusion for unanticipated input degradation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "on single source robustness in deep fusion models"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "evaluating bayesian deep learning methods for semantic segmentation"
    - "robust deep multi modal learning based on gated information fusion network"

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)