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: "mask propagation network for video object segmentation"
  Abstract: In this work, we propose a mask propagation network to treat the video segmentation problem as a concept of the guided instance segmentation. Similar to most MaskTrack based video segmentation methods, our method takes the mask probability map of previous frame and the appearance of current frame as inputs, and predicts the mask probability map for the current frame. Specifically, we adopt the Xception backbone based DeepLab v3+ model as the probability map predictor in our prediction pipeline. Besides, instead of the full image and the original mask probability, our network takes the region of interest of the instance, and the new mask probability which warped by the optical flow between the previous and current frames as the inputs. We also ensemble the modified One-Shot Video Segmentation Network to make the final predictions in order to retrieve and segment the missing instance.

Connected through hyperedge HE-1 (induced by source paper "meta learning with differentiable closed form solver for fast video object segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "meta learning with differentiable closed form solvers"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "prototypical networks for few shot learning"
    - "video object segmentation without temporal information"
    - "learning to remember rare events"
    - "online adaptation of convolutional neural networks for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "in defense of osvos"):
  with members:
    - "learning pairwise relationship for multi object detection in crowded scenes"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "fast and accurate online video object segmentation via tracking parts"
    - "yolov3 an incremental improvement"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "fast online object tracking and segmentation a unifying approach"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-3 (induced by source paper "towards good practices for video object segmentation"):
  with members:
    - "video object segmentation using space time memory networks"
    - "multi person pose estimation with enhanced channel wise and spatial information"
    - "youtube vos sequence to sequence video object segmentation"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)