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 learning from video to detect foreground objects in single images"
  Abstract: Unsupervised learning from visual data is one of the most difficult challenges in computer vision, being a fundamental task for understanding how visual recognition works. From a practical point of view, learning from unsupervised visual input has an immense practical value, as very large quantities of unlabeled videos can be collected at low cost. In this paper, we address the task of unsupervised learning to detect and segment foreground objects in single images. We achieve our goal by training a student pathway, consisting of a deep neural network. It learns to predict from a single input image (a video frame) the output for that particular frame, of a teacher pathway that performs unsupervised object discovery in video. Our approach is different from the published literature that performs unsupervised discovery in videos or in collections of images at test time. We move the unsupervised discovery phase during the training stage, while at test time we apply the standard feed-forward processing along the student pathway. This has a dual benefit: firstly, it allows in principle unlimited possibilities of learning and generalization during training, while remaining very fast at testing. Secondly, the student not only becomes able to detect in single images significantly better than its unsupervised video discovery teacher, but it also achieves state of the art results on two important current benchmarks, YouTube Objects and Object Discovery datasets. Moreover, at test time, our system is at least two orders of magnitude faster than other previous methods.

Connected through hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "motion attentive transition for zero shot video object segmentation"
    - "self supervised learning for video correspondence flow"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "multigrid predictive filter flow for unsupervised learning on videos"
    - "multi source weak supervision for saliency detection"
    - "learning correspondence from the cycle consistency of time"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of foreground object segmentation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "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-3 (induced by source paper "learning navigation by visual localization and trajectory prediction"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "closing the gap towards end to end autonomous vehicle system"
    - "learning accurate comfortable and human like driving"
    - "a multi stage multi task neural network for aerial scene interpretation and geolocalization"
    - "variational end to end navigation and localization"
    - "learning to navigate in cities without a map"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "geometric loss functions for camera pose regression with deep learning"

Connected through hyperedge HE-4 (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 object segmentation in video by efficient selection of highly probable positive features"
    - "video object segmentation without temporal information"
    - "learning video object segmentation with visual memory"

Connected through hyperedge HE-5 (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"
    - "unsupervised object segmentation in video by efficient selection of highly probable positive features"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "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-6 (induced by source paper "craft complementary recommendations using adversarial feature transformer"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "progressive growing of gans for improved quality stability and variation"
    - "be your own prada fashion synthesis with structural coherence"
    - "adversarial discriminative domain adaptation"
    - "multi task self supervised visual learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning fashion compatibility with bidirectional lstms"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-7 (induced by source paper "unsupervised learning for large scale fiber detection and tracking in microscopic material images"):
  with members:
    - "detect to track and track to detect"
    - "unsupervised representation learning by sorting sequences"
    - "learning to segment human by watching youtube"
    - "is faster r cnn doing well for pedestrian detection"
    - "finding tiny faces"
    - "unsupervised learning of edges"
    - "unsupervised learning of visual representations using videos"
    - "fast r cnn"

Connected through hyperedge HE-8 (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"
    - "training object class detectors with click supervision"
    - "ssd single shot multibox detector"
    - "click carving segmenting objects in video with point clicks"
    - "spot on action localization from pointly supervised proposals"
    - "deep interactive object selection"
    - "weakly supervised deep detection networks"
    - "adam a method for stochastic optimization"

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)