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: "improved image boundaries for better video segmentation"
  Abstract: Graph-based video segmentation methods rely on superpixels as starting point. While most previous work has focused on the construction of the graph edges and weights as well as solving the graph partitioning problem, this paper focuses on better superpixels for video segmentation. We demonstrate by a comparative analysis that superpixels extracted from boundaries perform best, and show that boundary estimation can be significantly improved via image and time domain cues. With superpixels generated from our better boundaries we observe consistent improvement for two video segmentation methods in two different datasets.

Connected through hyperedge HE-1 (induced by source paper "isec iterative over segmentation via edge clustering"):
  with members:
    - "superpatchmatch an algorithm for robust correspondences using superpixel patches"
    - "superpixels an evaluation of the state of the art"
    - "superpixel convolutional networks using bilateral inceptions"

Connected through hyperedge HE-2 (induced by source paper "boundary flow a siamese network that predicts boundary motion without training on motion"):
  with members:
    - "convolutional oriented boundaries"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation"
    - "holistically nested edge detection"
    - "high for low and low for high efficient boundary detection from deep object features and its applications to high level vision"
    - "learning deconvolution network for semantic segmentation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-3 (induced by source paper "long term image boundary prediction"):
  with members:
    - "visual interaction networks"
    - "learning to generate long term future via hierarchical prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "convolutional oriented boundaries"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "learning physical intuition of block towers by example"
    - "interaction networks for learning about objects relations and physics"
    - "visual stability prediction and its application to manipulation"

Connected through hyperedge HE-4 (induced by source paper "efficient convolutional neural network with binary quantization layer"):
  with members:
    - "semantic understanding of scenes through the ade20k dataset"
    - "cnn aware binary map for general semantic segmentation"
    - "plug and play cnn for crowd motion analysis an application in abnormal event detection"
    - "learning deconvolution network for semantic segmentation"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "finding action tubes"

Connected through hyperedge HE-5 (induced by source paper "cnn aware binary map for general semantic segmentation"):
  with members:
    - "semantic understanding of scenes through the ade20k dataset"
    - "conditional random fields as recurrent neural networks"
    - "learning deconvolution network for semantic segmentation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "finding action tubes"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal image boundary extrapolation"):
  with members:
    - "to fall or not to fall a visual approach to physical stability prediction"
    - "learning physical intuition of block towers by example"
    - "unsupervised learning of video representations using lstms"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "learning visual predictive models of physics for playing billiards"
    - "deep multi scale video prediction beyond mean square error"
    - "video language modeling a baseline for generative models of natural videos"

Connected through hyperedge HE-7 (induced by source paper "long term image boundary extrapolation"):
  with members:
    - "convolutional oriented boundaries"
    - "learning physical intuition of block towers by example"
    - "visual stability prediction and its application to manipulation"
    - "unsupervised learning of video representations using lstms"
    - "newtonian image understanding unfolding the dynamics of objects in static images"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "learning visual predictive models of physics for playing billiards"
    - "deep multi scale video prediction beyond mean square error"

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)