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: "large scale object discovery and detector adaptation from unlabeled video"
  Abstract: We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform. We propose a fully automatic approach for object mining from video which builds upon a generic object tracking approach. By applying this method to three large video datasets from autonomous driving and mobile robotics scenarios, we demonstrate its robustness and generality. Based on the object mining results, we propose a novel approach for unsupervised object discovery by appearance-based clustering. We show that this approach successfully discovers interesting objects relevant to driving scenarios. In addition, we perform self-supervised detector adaptation in order to improve detection performance on the KITTI dataset for existing categories. Our approach has direct relevance for enabling large-scale object learning for autonomous driving.

Connected through hyperedge HE-1 (induced by source paper "key instance selection for unsupervised video object segmentation"):
  with members:
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "video salient object detection via fully convolutional networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the 2018 davis challenge on video object segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-2 (induced by source paper "parallel total variation distance estimation with neural networks for merging over clusterings"):
  with members:
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "3d shapenets a deep representation for volumetric shapes"
    - "on integral probability metrics φ divergences and binary classification"

Connected through hyperedge HE-3 (induced by source paper "premvos proposal generation refinement and merging for 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"
    - "efficient video object segmentation via network modulation"
    - "youtube vos sequence to sequence video object segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "the 2018 davis challenge on video object segmentation"
    - "video object segmentation with joint re identification and attention aware mask propagation"
    - "video object segmentation with re identification"

Connected through hyperedge HE-4 (induced by source paper "a probabilistic constrained clustering for transfer learning and image category discovery"):
  with members:
    - "learning to cluster in order to transfer across domains and tasks"
    - "semi supervised kernel metric learning using relative comparisons"
    - "identity mappings in deep residual networks"
    - "deep image category discovery using a transferred similarity function"
    - "neural network based clustering using pairwise constraints"
    - "very deep convolutional networks for large scale image recognition"
    - "on constrained spectral clustering and its applications"

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)