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: "multi cue structure preserving mrf for unconstrained video segmentation"
  Abstract: Video segmentation is a stepping stone to understanding video context. Video segmentation enables one to represent a video by decomposing it into coherent regions which comprise whole or parts of objects. However, the challenge originates from the fact that most of the video segmentation algorithms are based on unsupervised learning due to expensive cost of pixelwise video annotation and intra-class variability within similar unconstrained video classes. We propose a Markov Random Field model for unconstrained video segmentation that relies on tight integration of multiple cues: vertices are defined from contour based superpixels, unary potentials from temporal smooth label likelihood and pairwise potentials from global structure of a video. Multi-cue structure is a breakthrough to extracting coherent object regions for unconstrained videos in absence of supervision. Our experiments on VSB100 dataset show that the proposed model significantly outperforms competing state-of-the-art algorithms. Qualitative analysis illustrates that video segmentation result of the proposed model is consistent with human perception of objects.

Connected through hyperedge HE-1 (induced by source paper "automatic real time background cut for portrait videos"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "stfcn spatio temporal fcn for semantic video segmentation"
    - "semantic video segmentation by gated recurrent flow propagation"
    - "unsupervised video segmentation via spatio temporally nonlocal appearance learning"
    - "recurrent fully convolutional networks for video segmentation"
    - "clockwork convnets for video semantic segmentation"
    - "one shot video object segmentation"
    - "semi supervised domain adaptation for weakly labeled semantic video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "point wise mutual information based video segmentation with high temporal consistency"):
  with members:
    - "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"
    - "fast r cnn"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "pushing the boundaries of boundary detection using deep learning"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "oriented edge forests for boundary detection"

Connected through hyperedge HE-3 (induced by source paper "improved image boundaries for better video segmentation"):
  with members:
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "point wise mutual information based video segmentation with high temporal consistency"
    - "holistically nested edge detection"
    - "convolutional feature masking for joint object and stuff segmentation"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "learning to segment moving objects in videos"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "oriented edge forests for boundary detection"

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)