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 topic modelling for weakly supervised object localisation"
  Abstract: We address the problem of localisation of objects as bounding boxes in images 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. We propose a novel framework based on Bayesian joint topic modelling. Our framework has three distinctive advantages over previous works: (1) All object classes and image backgrounds are modelled jointly together in a single generative model so that "explaining away" inference can resolve ambiguity and lead to better learning and localisation. (2) The Bayesian formulation of the model enables easy integration of prior knowledge about object appearance to compensate for limited supervision. (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. Extensive experiments on the challenging VOC dataset demonstrate that our approach outperforms the state-of-the-art competitors.

Connected through hyperedge HE-1 (induced by source paper "graph representation learning for audio music genre classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bottom up broadcast neural network for music genre classification"
    - "fast graph representation learning with pytorch geometric"
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for text classification"
    - "deepinf social influence prediction with deep learning"
    - "learning region features for object detection"

Connected through hyperedge HE-2 (induced by source paper "visual and semantic knowledge transfer for large scale semi supervised object detection"):
  with members:
    - "ssd single shot multibox detector"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "you only look once unified real time object detection"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "progressive representation adaptation for weakly supervised object localization"):
  with members:
    - "deep self taught learning for weakly supervised object localization"
    - "weakly supervised object localization using things and stuff transfer"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "hcp a flexible cnn framework for multi label image classification"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "yolo9000 better faster stronger"
    - "weakly supervised cascaded convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised spatial context networks"):
  with members:
    - "augmenting supervised neural networks with unsupervised objectives for large scale image classification"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"
    - "exploiting image trained cnn architectures for unconstrained video classification"
    - "you only look once unified real time object detection"
    - "unsupervised learning of visual representations using videos"

Connected through hyperedge HE-5 (induced by source paper "discover and learn new objects from documentaries"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "object detection from video tubelets with convolutional neural networks"
    - "segmentation from natural language expressions"
    - "grounding of textual phrases in images by reconstruction"
    - "track and transfer watching videos to simulate strong human supervision for weakly supervised object detection"
    - "generation and comprehension of unambiguous object descriptions"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "unsupervised learning from narrated instruction videos"

Connected through hyperedge HE-6 (induced by source paper "co localization with category consistent features and geodesic distance propagation"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "image co localization by mimicking a good detector s confidence score distribution"
    - "ssd single shot multibox detector"
    - "weakly supervised localization using deep feature maps"
    - "geodesic distance histogram feature for video segmentation"
    - "you only look once unified real time object detection"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "relaxed multiple instance svm with application to object discovery"

Connected through hyperedge HE-7 (induced by source paper "deep patch learning for weakly supervised object classification and discovery"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "deep filter banks for texture recognition description and segmentation"
    - "fast r cnn"
    - "relaxed multiple instance svm with application to object discovery"
    - "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals"
    - "exploit bounding box annotations for multi label object recognition"
    - "exemplar based deep discriminative and shareable feature learning for scene image classification"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "weakly and semi supervised object detection with expectation maximization algorithm"):
  with members:
    - "ssd single shot multibox detector"
    - "weakly supervised cascaded convolutional networks"
    - "weakly supervised object detection using pseudo strong labels"
    - "contextlocnet context aware deep network models for weakly supervised localization"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

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)