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: "unsupervised object segmentation in video by efficient selection of highly probable positive features"
  Abstract: We address an essential problem in computer vision, that of unsupervised object segmentation in video, where a main object of interest in a video sequence should be automatically separated from its background. An efficient solution to this task would enable large-scale video interpretation at a high semantic level in the absence of the costly manually labeled ground truth. We propose an efficient unsupervised method for generating foreground object soft-segmentation masks based on automatic selection and learning from highly probable positive features. We show that such features can be selected efficiently by taking into consideration the spatio-temporal, appearance and motion consistency of the object during the whole observed sequence. We also emphasize the role of the contrasting properties between the foreground object and its background. Our model is created in two stages: we start from pixel level analysis, on top of which we add a regression model trained on a descriptor that considers information over groups of pixels and is both discriminative and invariant to many changes that the object undergoes throughout the video. We also present theoretical properties of our unsupervised learning method, that under some mild constraints is guaranteed to learn a correct discriminative classifier even in the unsupervised case. Our method achieves competitive and even state of the art results on the challenging Youtube-Objects and SegTrack datasets, while being at least one order of magnitude faster than the competition. We believe that the competitive performance of our method in practice, along with its theoretical properties, constitute an important step towards solving unsupervised discovery in video.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of foreground object segmentation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "unsupervised representation learning by sorting sequences"
    - "segflow joint learning for video object segmentation and optical flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning representations for automatic colorization"
    - "learning motion patterns in videos"

Connected through hyperedge HE-2 (induced by source paper "online object representations with contrastive learning"):
  with members:
    - "self supervisory signals for object discovery and detection"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "superpoint self supervised interest point detection and description"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "object category learning and retrieval with weak supervision"
    - "deep image prior"
    - "mixup beyond empirical risk minimization"
    - "time contrastive networks self supervised learning from video"

Connected through hyperedge HE-3 (induced by source paper "spacetime graph optimization for video object segmentation"):
  with members:
    - "unsupervised online video object segmentation with motion property understanding"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "blazingly fast video object segmentation with pixel wise metric learning"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "video object segmentation without temporal information"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-4 (induced by source paper "adversarial framework for unsupervised learning of motion dynamics in videos"):
  with members:
    - "video to video synthesis"
    - "video prediction with appearance and motion conditions"
    - "tracking emerges by colorizing videos"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "decomposing motion and content for natural video sequence prediction"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "hierarchical video generation from orthogonal information optical flow and texture"
    - "data distillation towards omni supervised learning"

Connected through hyperedge HE-5 (induced by source paper "towards accurate generative models of video a new metric challenges"):
  with members:
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "self supervised visual planning with temporal skip connections"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "coarse to fine semantic segmentation from image level labels"):
  with members:
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "decoupled spatial neural attention for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "unsupervised learning from video to detect foreground objects in single images"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "weakly supervised semantic segmentation based on web image co segmentation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "segflow joint learning for video object segmentation and optical flow"

Connected through hyperedge HE-8 (induced by source paper "learning a robust society of tracking parts using co occurrence constraints"):
  with members:
    - "eco efficient convolution operators for tracking"
    - "beyond local search tracking objects everywhere with instance specific proposals"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "discriminative scale space tracking"
    - "modeling and propagating cnns in a tree structure for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"
    - "staple complementary learners for real time tracking"

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)