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: "weakly supervised semantic segmentation using motion cues"
  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.

Connected through hyperedge HE-1 (induced by source paper "main multi attention instance network for video segmentation"):
  with members:
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"
    - "efficient video object segmentation via network modulation"
    - "maskrnn instance level video object segmentation"
    - "videomatch matching based video object segmentation"
    - "fast online object tracking and segmentation a unifying approach"
    - "youtube vos a large scale video object segmentation benchmark"
    - "video object segmentation with joint re identification and attention aware mask propagation"

Connected through hyperedge HE-2 (induced by source paper "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "deeply supervised salient object detection with short connections"
    - "learning a discriminative feature network for semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "adversarial complementary learning for weakly supervised object localization"
    - "robust tumor localization with pyramid grad cam"

Connected through hyperedge HE-3 (induced by source paper "self supervised difference detection for weakly supervised semantic segmentation"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "frame to frame aggregation of active regions in web videos for weakly supervised semantic segmentation"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "tell me where to look guided attention inference network"
    - "self erasing network for integral object attention"
    - "weakly supervised semantic segmentation using web crawled videos"

Connected through hyperedge HE-4 (induced by source paper "towards object detection from motion"):
  with members:
    - "online object representations with contrastive learning"
    - "grasp2vec learning object representations from self supervised grasping"
    - "learning instance segmentation by interaction"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "pves position velocity encoders for unsupervised learning of structured state representations"
    - "time contrastive networks self supervised learning from video"
    - "object centric representation learning from unlabeled videos"
    - "learning features by watching objects move"

Connected through hyperedge HE-5 (induced by source paper "incorporating network built in priors in weakly supervised semantic segmentation"):
  with members:
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "weakly supervised semantic labelling and instance segmentation"
    - "deep hierarchical parsing for semantic segmentation"
    - "conditional random fields as recurrent neural networks"
    - "boxsup exploiting bounding boxes to supervise convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"):
  with members:
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "exploiting saliency for object segmentation from image level labels"
    - "multiple instance detection network with online instance classifier refinement"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"):
  with members:
    - "weakly supervised semantic segmentation using web crawled videos"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bottom up top down cues for weakly supervised semantic segmentation"
    - "video scene parsing with predictive feature learning"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "built in foreground background prior for weakly supervised semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "minimizing supervision for free space segmentation"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "bringing background into the foreground making all classes equal in weakly supervised video semantic segmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "weakly supervised semantic segmentation using web crawled videos"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "dilated residual networks"
    - "chainercv a library for deep learning in computer vision"
    - "mask r cnn"

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)