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: "robust subjective visual property prediction from crowdsourced pairwise labels"
  Abstract: The problem of estimating subjective visual properties from image and video has attracted increasing interest. A subjective visual property is useful either on its own (e.g. image and video interestingness) or as an intermediate representation for visual recognition (e.g. a relative attribute). Due to its ambiguous nature, annotating the value of a subjective visual property for learning a prediction model is challenging. To make the annotation more reliable, recent studies employ crowdsourcing tools to collect pairwise comparison labels. However, using crowdsourced data also introduces outliers. Existing methods rely on majority voting to prune the annotation outliers/errors. They thus require a large amount of pairwise labels to be collected. More importantly as a local outlier detection method, majority voting is ineffective in identifying outliers that can cause global ranking inconsistencies. In this paper, we propose a more principled way to identify annotation outliers by formulating the subjective visual property prediction task as a unified robust learning to rank problem, tackling both the outlier detection and learning to rank jointly. This differs from existing methods in that (1) the proposed method integrates local pairwise comparison labels together to minimise a cost that corresponds to global inconsistency of ranking order, and (2) the outlier detection and learning to rank problems are solved jointly. This not only leads to better detection of annotation outliers but also enables learning with extremely sparse annotations.

Connected through hyperedge HE-1 (induced by source paper "scalable bayesian preference learning for crowds"):
  with members:
    - "bayesian heatmaps probabilistic classification with multiple unreliable information sources"
    - "hybrid mst a hybrid active sampling strategy for pairwise preference aggregation"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "distributed bayesian probabilistic matrix factorization"
    - "distributed bayesian probabilistic matrix factorization"

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"
    - "deep residual learning for image recognition"
    - "discovering attribute shades of meaning with the crowd"

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"
    - "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"
    - "attribute2image conditional image generation from visual attributes"

Connected through hyperedge HE-4 (induced by source paper "heterogeneous knowledge transfer in video emotion recognition attribution and summarization"):
  with members:
    - "transductive multi view zero shot learning"
    - "robust image sentiment analysis using progressively trained and domain transferred deep networks"
    - "multi view metric learning for multi view video summarization"
    - "going deeper with convolutions"
    - "return of the devil in the details delving deep into convolutional nets"
    - "deepsentibank visual sentiment concept classification with deep convolutional neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "visual sentiment prediction with deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "from social to individuals a parsimonious path of multi level models for crowdsourced preference aggregation"):
  with members:
    - "adversarial top k ranking"
    - "parsimonious mixed effects hodgerank for crowdsourced preference aggregation"
    - "cdas a crowdsourcing data analytics system"
    - "flows and decompositions of games harmonic and potential games"

Connected through hyperedge HE-6 (induced by source paper "learning from weak and noisy labels for semantic segmentation"):
  with members:
    - "joint calibration for semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "simultaneous detection and segmentation"
    - "fully convolutional multi class multiple instance learning"
    - "from image level to pixel level labeling with convolutional networks"
    - "fully convolutional networks for semantic segmentation"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "image and video mining through online learning"):
  with members:
    - "return of the devil in the details delving deep into convolutional nets"
    - "caffe convolutional architecture for fast feature embedding"
    - "beyond gaussian pyramid multi skip feature stacking for action recognition"

Connected through hyperedge HE-8 (induced by source paper "parsimonious mixed effects hodgerank for crowdsourced preference aggregation"):
  with members:
    - "flows and decompositions of games harmonic and potential games"
    - "active ranking using pairwise comparisons"

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)