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: "object cosegmentation using deep siamese network"
  Abstract: Object cosegmentation addresses the problem of discovering similar objects from multiple images and segmenting them as foreground simultaneously. In this paper, we propose a novel end-to-end pipeline to segment the similar objects simultaneously from relevant set of images using supervised learning via deep-learning framework. We experiment with multiple set of object proposal generation techniques and perform extensive numerical evaluations by training the Siamese network with generated object proposals. Similar objects proposals for the test images are retrieved using the ANNOY (Approximate Nearest Neighbor) library and deep semantic segmentation is performed on them. Finally, we form a collage from the segmented similar objects based on the relative importance of the objects.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised lesion segmentation on ct scans using co segmentation"):
  with members:
    - "weakly supervised lesion co segmentation on ct scans"
    - "xlsor a robust and accurate lung segmentor on chest x rays using criss cross attention and customized radiorealistic abnormalities generation"
    - "abnormal chest x ray identification with generative adversarial one class classifier"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "ct image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"

Connected through hyperedge HE-2 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "boosted attention leveraging human attention for image captioning"
    - "object part attention model for fine grained image classification"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "semantic aware attention based deep object co segmentation"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "cross view image synthesis using conditional gans"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-3 (induced by source paper "deep object co segmentation via spatial semantic network modulation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "saliency for fine grained object recognition in domains with scarce training data"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "cross modulation networks for few shot learning"
    - "efficient video object segmentation via network modulation"
    - "deep object co segmentation"

Connected through hyperedge HE-4 (induced by source paper "post mortem iris recognition resistant to biological eye decay processes"):
  with members:
    - "domain specific human inspired binarized statistical image features for iris recognition"
    - "presentation attack detection for cadaver iris"
    - "iris recognition after death"
    - "perception of image features in post mortem iris recognition humans vs machines"
    - "superpoint self supervised interest point detection and description"
    - "lift learned invariant feature transform"
    - "human iris recognition in post mortem subjects study and database"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-5 (induced by source paper "semantic aware attention based deep object co segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "show attend and translate unsupervised image translation with self regularization and attention"
    - "deep object co segmentation"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "vqs linking segmentations to questions and answers for supervised attention in vqa and question focused semantic segmentation"
    - "squeeze and excitation networks"
    - "structured attentions for visual question answering"
    - "bottom up and top down attention for image captioning and visual question answering"

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)