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: "libsvx a supervoxel library and benchmark for early video processing"
  Abstract: Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study seven supervoxel algorithms, including both off-line and streaming methods, in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of seven quality metrics to measure these desirable supervoxel characteristics. In addition, we evaluate the methods in a supervoxel classification task as a proxy for subsequent high-level uses of the supervoxels in video analysis. We use six existing benchmark video datasets with a variety of content-types and dense human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) and temporal superpixels (TSP) methods are the top-performers among the seven methods. They all perform well in terms of segmentation accuracy, but vary in regard to the other desiderata: GBH captures object boundaries best; SWA has the best potential for region compression; and TSP achieves the best undersegmentation error.

Connected through hyperedge HE-1 (induced by source paper "tukey inspired video object segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "efficient video object segmentation via network modulation"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "video object segmentation using supervoxel based gerrymandering"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"

Connected through hyperedge HE-2 (induced by source paper "learning to segment moving objects"):
  with members:
    - "learning video object segmentation with visual memory"
    - "unsupervised learning for physical interaction through video prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "learning motion patterns in videos"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "identity mappings in deep residual networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "action understanding with multiple classes of actors"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "online action detection"
    - "end to end learning of action detection from frame glimpses in videos"
    - "instance aware semantic segmentation via multi task network cascades"
    - "actor action semantic segmentation with grouping process models"
    - "two stream convolutional networks for action recognition in videos"
    - "a study of actor and action semantic retention in video supervoxel segmentation"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

Connected through hyperedge HE-4 (induced by source paper "video object segmentation using supervoxel based gerrymandering"):
  with members:
    - "fusionseg learning to combine motion and appearance for fully automatic segmention of generic objects in videos"
    - "learning video object segmentation from static images"
    - "video propagation networks"

Connected through hyperedge HE-5 (induced by source paper "learning motion patterns in videos"):
  with members:
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "learning to refine object segments"
    - "it s moving a probabilistic model for causal motion segmentation in moving camera videos"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "hypercolumns for object segmentation and fine grained localization"

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)