human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "weakly supervised few shot object segmentation using co attention with visual and semantic inputs"):
  Abstract: Significant progress has been made recently in developing few-shot object segmentation methods. Learning is shown to be successful in few segmentation settings, including pixel-level, scribbles and bounding boxes. This paper takes another approach, i.e., only requiring image-level classification data for few-shot object segmentation. We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embedding. Our model using image-level labels achieves 4.8% improvement over previously proposed image-level few-shot object segmentation. It also outperforms state-of-the-art methods that use weak bounding box supervision on PASCAL-$5^i$. Our results show that few-shot segmentation benefits from utilizing word embeddings, and that we are able to perform few-shot segmentation using stacked joint visual semantic processing with weak image-level labels. We further propose a novel setup, Temporal Object Segmentation for Few-shot Learning (TOSFL) for videos. TOSFL requires only image-level labels for the first frame in order to segment objects in the following frames. TOSFL provides a novel benchmark for video segmentation, which can be used on a variety of public video data such as Youtube-VOS, as demonstrated in our experiment.

Includes members:
  Member "see more know more unsupervised video object segmentation with co attention siamese networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "zero shot video object segmentation via attentive graph neural networks")
  Member "one shot object detection with co attention and co excitation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "os2d one stage one shot object detection by matching anchor features")
  Member "online object representations with contrastive learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neurofabric identifying ideal topologies for training a priori sparse networks")
  Member "adaptive masked proxies for few shot segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the texture bias for few shot cnn segmentation")
  Member "panet few shot image semantic segmentation with prototype alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "youtube vos a large scale video object segmentation benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "online object and task learning via human robot interaction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "visual geometric skill inference by watching human demonstration")

Question: Please tell me which class the source paper 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)