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: "learning semantic segmentation with weakly annotated videos"
  Abstract: Fully convolutional neural networks (FCNNs) trained on a large number of images with strong pixel-level annotations have become the new state of the art for the semantic segmentation task. While there have been recent attempts to learn FCNNs from image-level weak annotations , they need additional constraints, such as the size of an object, to obtain reasonable performance. To address this issue, we present motion-CNN (M-CNN), a novel FCNN framework which incorporates motion cues and is learned from video-level weak annotations. Our learning scheme to train the network uses motion segments as soft constraints, thereby handling noisy motion information. When trained on weakly-annotated videos, our method outperforms the state-of-the-art EM-Adapt approach on the PASCAL VOC 2012 image segmentation benchmark. We also demonstrate that the performance of M-CNN learned with 150 weak video annotations is on par with state-of-the-art weakly-supervised methods trained with thousands of images. Finally, M-CNN substantially outperforms recent approaches in a related task of video co-localization on the YouTube-Objects dataset. This is an extended version of our ECCV 2016 paper.

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 object segmentation in video by efficient selection of highly probable positive features"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised learning of instance segmentation with inter pixel relations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weakly supervised instance segmentation using class peak response"
    - "learning to segment via cut and paste"
    - "group normalization"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised semantic segmentation using web crawled videos"):
  with members:
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "object detection tracking and motion segmentation for object level video segmentation"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "semantic image segmentation via deep parsing network"
    - "webly supervised learning of convolutional networks"

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)