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: "efficient hierarchical graph based segmentation of rgbd videos"
  Abstract: We present an efficient and scalable algorithm for segmenting 3D RGBD point clouds by combining depth, color, and temporal information using a multistage, hierarchical graph-based approach. Our algorithm processes a moving window over several point clouds to group similar regions over a graph, resulting in an initial over-segmentation. These regions are then merged to yield a dendrogram using agglomerative clustering via a minimum spanning tree algorithm. Bipartite graph matching at a given level of the hierarchical tree yields the final segmentation of the point clouds by maintaining region identities over arbitrarily long periods of time. We show that a multistage segmentation with depth then color yields better results than a linear combination of depth and color. Due to its incremental processing, our algorithm can process videos of any length and in a streaming pipeline. The algorithm's ability to produce robust, efficient segmentation is demonstrated with numerous experimental results on challenging sequences from our own as well as public RGBD data sets.

Connected through hyperedge HE-1 (induced by source paper "gated2depth real time dense lidar from gated images"):
  with members:
    - "unsupervised adversarial depth estimation using cycled generative networks"
    - "rethinking monocular depth estimation with adversarial training"
    - "self supervised sparse to dense self supervised depth completion from lidar and monocular camera"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "geometry meets semantics for semi supervised monocular depth estimation"
    - "estimating depth from rgb and sparse sensing"
    - "pyramid stereo matching network"
    - "sparse to dense depth prediction from sparse depth samples and a single image"

Connected through hyperedge HE-2 (induced by source paper "ovsnet towards one pass real time video object segmentation"):
  with members:
    - "path aggregation network for instance segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "a spectral approach to unsupervised object segmentation in video"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "one shot video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "towards segmenting consumer stereo videos benchmark baselines and ensembles"):
  with members:
    - "geometric context from videos"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "semantic instance labeling leveraging hierarchical segmentation"):
  with members:
    - "learning rich features from rgb d images for object detection and segmentation"
    - "indoor semantic segmentation using depth information"

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)