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: "discovering attribute shades of meaning with the crowd"
  Abstract: To learn semantic attributes, existing methods typically train one discriminative model for each word in a vocabulary of nameable properties. However, this "one model per word" assumption is problematic: while a word might have a precise linguistic definition, it need not have a precise visual definition. We propose to discover shades of attribute meaning. Given an attribute name, we use crowdsourced image labels to discover the latent factors underlying how different annotators perceive the named concept. We show that structure in those latent factors helps reveal shades, that is, interpretations for the attribute shared by some group of annotators. Using these shades, we train classifiers to capture the primary (often subtle) variants of the attribute. The resulting models are both semantic and visually precise. By catering to users' interpretations, they improve attribute prediction accuracy on novel images. Shades also enable more successful attribute-based image search, by providing robust personalized models for retrieving multi-attribute query results. They are widely applicable to tasks that involve describing visual content, such as zero-shot category learning and organization of photo collections.

Connected through hyperedge HE-1 (induced by source paper "why does a visual question have different answers"):
  with members:
    - "vizwiz grand challenge answering visual questions from blind people"
    - "crowdsourcing ground truth for medical relation extraction"
    - "an analysis of visual question answering algorithms"
    - "learning to disambiguate by asking discriminative questions"
    - "the promise of premise harnessing question premises in visual question answering"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "crowdsourcing question answer meaning representations"
    - "bottom up and top down attention for image captioning and visual question answering"

Connected through hyperedge HE-2 (induced by source paper "deep robust subjective visual property prediction in crowdsourcing"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "learning from weak and noisy labels for semantic segmentation"
    - "learning deep networks from noisy labels with dropout regularization"
    - "deep learning from noisy image labels with quality embedding"
    - "toward robustness against label noise in training deep discriminative neural networks"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "compare and contrast learning prominent visual differences"):
  with members:
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "fashion forward forecasting visual style in fashion"
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "shallow and deep convolutional networks for saliency prediction"
    - "end to end localization and ranking for relative attributes"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "generation and comprehension of unambiguous object descriptions"
    - "understanding image virality"

Connected through hyperedge HE-4 (induced by source paper "image retrieval with mixed initiative and multimodal feedback"):
  with members:
    - "image to image translation with conditional adversarial networks"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "end to end learning of action detection from frame glimpses in videos"
    - "cross domain image retrieval with a dual attribute aware ranking network"
    - "attribute graph a graph based approach to image ranking"
    - "deep reinforcement learning with double q learning"
    - "reinforcement learning a survey"

Connected through hyperedge HE-5 (induced by source paper "automatic understanding of image and video advertisements"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep reconstruction classification networks for unsupervised domain adaptation"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "what happens if learning to predict the effect of forces in images"
    - "learning aligned cross modal representations from weakly aligned data"
    - "domain separation networks"
    - "unsupervised domain adaptation with residual transfer networks"
    - "whittlesearch interactive image search with relative attribute feedback"

Connected through hyperedge HE-6 (induced by source paper "training object class detectors with click supervision"):
  with members:
    - "transfer learning by ranking for weakly supervised object annotation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "click carving segmenting objects in video with point clicks"
    - "spot on action localization from pointly supervised proposals"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "embracing error to enable rapid crowdsourcing"
    - "contextlocnet context aware deep network models for weakly supervised localization"

Connected through hyperedge HE-7 (induced by source paper "extreme clicking for efficient object annotation"):
  with members:
    - "a glimpse far into the future understanding long term crowd worker quality"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "click carving segmenting objects in video with point clicks"
    - "we don t need no bounding boxes training object class detectors using only human verification"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "weakly supervised deep detection networks"
    - "fast r cnn"
    - "analysing domain shift factors between videos and images for object detection"

Connected through hyperedge HE-8 (induced by source paper "predicting foreground object ambiguity and efficiently crowdsourcing the segmentation s"):
  with members:
    - "measuring and predicting tag importance for image retrieval"
    - "salient object subitizing"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "salient object detection a benchmark"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "microsoft coco common objects in context"
    - "fast edge detection using structured forests"

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)