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: "rgbd salient object detection via deep fusion"
  Abstract: Numerous efforts have been made to design various low-level saliency cues for RGBD saliency detection, such as color and depth contrast features as well as background and color compactness priors. However, how these low-level saliency cues interact with each other and how they can be effectively incorporated to generate a master saliency map remain challenging problems. In this paper, we design a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection. In contrast to existing works, in which raw image pixels are fed directly to the CNN, the proposed method takes advantage of the knowledge obtained in traditional saliency detection by adopting various flexible and interpretable saliency feature vectors as inputs. This guides the CNN to learn a combination of existing features to predict saliency more effectively, which presents a less complex problem than operating on the pixels directly. We then integrate a superpixel-based Laplacian propagation framework with the trained CNN to extract a spatially consistent saliency map by exploiting the intrinsic structure of the input image. Extensive quantitative and qualitative experimental evaluations on three data sets demonstrate that the proposed method consistently outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "rethinking rgb d salient object detection models datasets and large scale benchmarks"
    - "a simple pooling based design for real time salient object detection"
    - "egnet edge guidance network for salient object detection"
    - "pdnet prior model guided depth enhanced network for salient object detection"
    - "non local neural networks"
    - "attention is all you need"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-2 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"
    - "learning 3d object categories by looking around them"

Connected through hyperedge HE-3 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-4 (induced by source paper "rgb d image based object detection from traditional methods to deep learning techniques"):
  with members:
    - "improving image based localization with deep learning the impact of the loss function"
    - "deeply supervised salient object detection with short connections"
    - "geometric loss functions for camera pose regression with deep learning"
    - "numerical inversion of srnf maps for elastic shape analysis of genus zero surfaces"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-5 (induced by source paper "cnn based rgb d salient object detection learn select and fuse"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "modality distillation with multiple stream networks for action recognition"
    - "an iterative co saliency framework for rgbd images"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "salient object detection a benchmark"

Connected through hyperedge HE-6 (induced by source paper "adaptive fusion for rgb d salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "agile amulet real time salient object detection with contextual attention"
    - "vision based real time aerial object localization and tracking for uav sensing system"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "visual saliency based on multiscale deep features"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "salient object detection a distinctive feature integration model"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "video salient object detection via fully convolutional networks"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"

Connected through hyperedge HE-8 (induced by source paper "three birds one stone a general architecture for salient object segmentation edge detection and skeleton extraction"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"
    - "instance level salient object segmentation"

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)