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: "click carving segmenting objects in video with point clicks"
  Abstract: We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest. Whereas conventional interactive pipelines take the user's initialization as a starting point, we show the value in the system taking the lead even in initialization. In particular, for a given video frame, the system precomputes a ranked list of thousands of possible segmentation hypotheses (also referred to as object region proposals) using image and motion cues. Then, the user looks at the top ranked proposals, and clicks on the object boundary to carve away erroneous ones. This process iterates (typically 2-3 times), and each time the system revises the top ranked proposal set, until the user is satisfied with a resulting segmentation mask. Finally, the mask is propagated across the video to produce a spatio-temporal object tube. On three challenging datasets, we provide extensive comparisons with both existing work and simpler alternative methods. In all, the proposed Click Carving approach strikes an excellent balance of accuracy and human effort. It outperforms all similarly fast methods, and is competitive or better than those requiring 2 to 12 times the effort.

Connected through hyperedge HE-1 (induced by source paper "learning to track any object"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "tracking without bells and whistles"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-2 (induced by source paper "adaptive roi generation for video object segmentation using reinforcement learning"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "correlation filter selection for visual tracking using reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "the 2017 davis challenge on video object segmentation"
    - "video object segmentation without temporal information"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-3 (induced by source paper "geometry in active learning for binary and multi class image segmentation"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning algorithms for active learning"
    - "annotating object instances with a polygon rnn"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "becoming the expert interactive multi class machine teaching"
    - "active learning for delineation of curvilinear structures"
    - "what s the point semantic segmentation with point supervision"
    - "material recognition in the wild with the materials in context database"

Connected through hyperedge HE-4 (induced by source paper "fluid annotation a human machine collaboration interface for full image annotation"):
  with members:
    - "panoptic segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "deep extreme cut from extreme points to object segmentation"
    - "extreme clicking for efficient object annotation"
    - "annotating object instances with a polygon rnn"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-5 (induced by source paper "blazingly fast video object segmentation with pixel wise metric learning"):
  with members:
    - "efficient video object segmentation via network modulation"
    - "road reality oriented adaptation for semantic segmentation of urban scenes"
    - "the 2017 davis challenge on video object segmentation"
    - "video object segmentation without temporal information"
    - "semantically guided video object segmentation"
    - "one shot learning for semantic segmentation"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"

Connected through hyperedge HE-6 (induced by source paper "one click annotation with guided hierarchical object detection"):
  with members:
    - "few shot learning with metric agnostic conditional embeddings"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to compare relation network for few shot learning"
    - "annotating object instances with a polygon rnn"
    - "learning intelligent dialogs for bounding box annotation"

Connected through hyperedge HE-7 (induced by source paper "clickbait v2 training an object detector in real time"):
  with members:
    - "clickbait click based accelerated incremental training of convolutional neural networks"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "training object class detectors with click supervision"
    - "ssd single shot multibox detector"
    - "spot on action localization from pointly supervised proposals"
    - "deep interactive object selection"
    - "weakly supervised deep detection networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "lucid data dreaming for video object segmentation"):
  with members:
    - "deep spatio temporal random fields for efficient video segmentation"
    - "premvos proposal generation refinement and merging for video object segmentation"
    - "video object segmentation with re identification"
    - "the 2017 davis challenge on video object segmentation"
    - "super trajectory for video segmentation"
    - "guided optical flow learning"
    - "learning video object segmentation with visual memory"
    - "learning from synthetic humans"

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)