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: "bayesian joint modelling for object localisation in weakly labelled images"
  Abstract: We address the problem of localisation of objects as bounding boxes in images and videos with weak labels. This weakly supervised object localisation problem has been tackled in the past using discriminative models where each object class is localised independently from other classes. In this paper, a novel framework based on Bayesian joint topic modelling is proposed, which differs significantly from the existing ones in that: (1) All foreground object classes are modelled jointly in a single generative model that encodes multiple object co-existence so that "explaining away" inference can resolve ambiguity and lead to better learning and localisation. (2) Image backgrounds are shared across classes to better learn varying surroundings and "push out" objects of interest. (3) Our model can be learned with a mixture of weakly labelled and unlabelled data, allowing the large volume of unlabelled images on the Internet to be exploited for learning. Moreover, the Bayesian formulation enables the exploitation of various types of prior knowledge to compensate for the limited supervision offered by weakly labelled data, as well as Bayesian domain adaptation for transfer learning. Extensive experiments on the PASCAL VOC, ImageNet and YouTube-Object videos datasets demonstrate the effectiveness of our Bayesian joint model for weakly supervised object localisation.

Connected through hyperedge HE-1 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "object aware instance labeling for weakly supervised object detection"
    - "few shot object detection with attention rpn and multi relation detector"
    - "few example object detection with model communication"
    - "feature weighting and boosting for few shot segmentation"
    - "label propagation for deep semi supervised learning"
    - "adaptive masked proxies for few shot segmentation"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "pcl proposal cluster learning for weakly supervised object detection"):
  with members:
    - "self produced guidance for weakly supervised object localization"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deep self taught learning for weakly supervised object localization"
    - "weakly supervised object localization using things and stuff transfer"
    - "single shot object detection with enriched semantics"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-3 (induced by source paper "learning and refining of privileged information based rnns for action recognition from depth sequences"):
  with members:
    - "disturblabel regularizing cnn on the loss layer"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "tensor representations via kernel linearization for action recognition from 3d skeletons"
    - "attend infer repeat fast scene understanding with generative models"
    - "real time online action detection forests using spatio temporal contexts"
    - "bayesian dark knowledge"
    - "a recurrent latent variable model for sequential data"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised object localization using things and stuff transfer"):
  with members:
    - "transfer learning by ranking for weakly supervised object annotation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "weakly supervised object localization using size estimates"
    - "weakly supervised deep detection networks"
    - "fast r cnn"
    - "learning deconvolution network for semantic segmentation"
    - "on learning to localize objects with minimal supervision"
    - "weakly supervised discovery of visual pattern configurations"

Connected through hyperedge HE-5 (induced by source paper "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"):
  with members:
    - "transfer learning by ranking for weakly supervised object annotation"
    - "salient object detection via augmented hypotheses"
    - "on learning to localize objects with minimal supervision"
    - "weakly supervised discovery of visual pattern configurations"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning the semantic structure of objects from web supervision"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "unsupervised visual representation learning by context prediction"
    - "webly supervised learning of convolutional networks"
    - "weakly supervised deep detection networks"
    - "joint object and part segmentation using deep learned potentials"
    - "mid level deep pattern mining"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised object localization using size estimates"):
  with members:
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"
    - "on learning to localize objects with minimal supervision"
    - "weakly supervised discovery of visual pattern configurations"
    - "lsda large scale detection through adaptation"

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)