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: "video object segmentation using teacher student adaptation in a human robot interaction hri setting"
  Abstract: Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments, since the total number of objects and their variations can be intractable. Inspired by the children learning process, human robot interaction (HRI) can be utilized to teach robots about the world guided by humans similar to how children learn from a parent or a teacher. A human teacher can show potential objects of interest to the robot, which is able to self adapt to the teaching signal without providing manual segmentation labels. We propose a novel teacher-student learning paradigm to teach robots about their surrounding environment. A two-stream motion and appearance "teacher" network provides pseudo-labels to adapt an appearance "student" network. The student network is able to segment the newly learned objects in other scenes, whether they are static or in motion. We also introduce a carefully designed dataset that serves the proposed HRI setup, denoted as (I)nteractive (V)ideo (O)bject (S)egmentation. Our IVOS dataset contains teaching videos of different objects, and manipulation tasks. Unlike previous datasets, IVOS provides manipulation tasks sequences with segmentation annotation along with the waypoints for the robot trajectories. It also provides segmentation annotation for the different transformations such as translation, scale, planar rotation, and out-of-plane rotation. Our proposed adaptation method outperforms the state-of-the-art on DAVIS and FBMS with 6.8% and 1.2% in F-measure respectively. It improves over the baseline on IVOS dataset with 46.1% and 25.9% in mIoU.

Connected through hyperedge HE-1 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos"):
  with members:
    - "zero shot video object segmentation via attentive graph neural networks"
    - "key instance selection for unsupervised video object segmentation"
    - "large scale object mining for object discovery from unlabeled video"
    - "an efficient 3d cnn for action object segmentation in video"
    - "video instance segmentation"
    - "video object segmentation using space time memory networks"
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "dmm net differentiable mask matching network for video object segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "zero shot video object segmentation via attentive graph neural networks"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "reasoning visual dialogs with structural and partial observations"
    - "rvos end to end recurrent network for video object segmentation"
    - "ranet ranking attention network for fast video object segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "unsupervised video object segmentation using motion saliency guided spatio temporal propagation"
    - "learning human object interactions by graph parsing neural networks"

Connected through hyperedge HE-4 (induced by source paper "understanding contexts inside robot and human manipulation tasks through a vision language model and ontology system in a video stream"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "combined task and action learning from human demonstrations for mobile manipulation applications"
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "robot learning and execution of collaborative manipulation plans from youtube videos"
    - "integrating knowledge and reasoning in image understanding"
    - "improving grounded natural language understanding through human robot dialog"
    - "learning actions from human demonstration video for robotic manipulation"
    - "prospection interpretable plans from language by predicting the future"

Connected through hyperedge HE-5 (induced by source paper "motion attentive transition for zero shot video object segmentation"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "zero shot video object segmentation via attentive graph neural networks"
    - "rvos end to end recurrent network for video object segmentation"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "salient objects in clutter bringing salient object detection to the foreground"
    - "cbam convolutional block attention module"
    - "learning to segment moving objects"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-6 (induced by source paper "anchor diffusion for unsupervised video object segmentation"):
  with members:
    - "the 2019 davis challenge on vos unsupervised multi object segmentation"
    - "deeply supervised salient object detection with short connections"
    - "fast and accurate online video object segmentation via tracking parts"
    - "instance embedding transfer to unsupervised video object segmentation"
    - "video salient object detection via fully convolutional networks"
    - "fast online object tracking and segmentation a unifying approach"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tukey inspired video object segmentation"

Connected through hyperedge HE-7 (induced by source paper "an efficient 3d cnn for action object segmentation in video"):
  with members:
    - "videocapsulenet a simplified network for action detection"
    - "actor and action video segmentation from a sentence"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "learning to segment moving objects"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "action tubelet detector for spatio temporal action localization"

Connected through hyperedge HE-8 (induced by source paper "adaptive masked proxies for few shot segmentation"):
  with members:
    - "three scenarios for continual learning"
    - "deep clustering for unsupervised learning of visual features"
    - "sg one similarity guidance network for one shot semantic segmentation"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "no fuss distance metric learning using proxies"
    - "transfer of view manifold learning to similarity perception of novel objects"
    - "one shot learning for semantic segmentation"

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)