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: "exploit bounding box annotations for multi label object recognition"
  Abstract: Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal. In this paper, we incorporate local information to enhance the feature discriminative power. In particular, we first extract object proposals from each image. With each image treated as a bag and object proposals extracted from it treated as instances, we transform the multi-label recognition problem into a multi-class multi-instance learning problem. Then, in addition to extracting the typical CNN feature representation from each proposal, we propose to make use of ground-truth bounding box annotations (strong labels) to add another level of local information by using nearest-neighbor relationships of local regions to form a multi-view pipeline. The proposed multi-view multi-instance framework utilizes both weak and strong labels effectively, and more importantly it has the generalization ability to even boost the performance of unseen categories by partial strong labels from other categories. Our framework is extensively compared with state-of-the-art hand-crafted feature based methods and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and the generalization ability of the proposed framework. With strong labels, our framework is able to achieve state-of-the-art results in both datasets.

Connected through hyperedge HE-1 (induced by source paper "improving label ranking ensembles using boosting techniques"):
  with members:
    - "learning to order things"

Connected through hyperedge HE-2 (induced by source paper "elucidating image to set prediction an analysis of models losses and datasets"):
  with members:
    - "on the iterative refinement of densely connected representation levels for semantic segmentation"
    - "multi label learning with missing labels using mixed dependency graphs"
    - "exploring the limits of weakly supervised pretraining"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "loss functions for multiset prediction"
    - "attention is all you need"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "learning deep latent spaces for multi label classification"

Connected through hyperedge HE-3 (induced by source paper "cross modality attention with semantic graph embedding for multi label classification"):
  with members:
    - "multi label image recognition with graph convolutional networks"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "approach for video classification with multi label on youtube 8m dataset"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "order free rnn with visual attention for multi label classification"
    - "attention clusters purely attention based local feature integration for video classification"

Connected through hyperedge HE-4 (induced by source paper "detecting 11k classes large scale object detection without fine grained bounding boxes"):
  with members:
    - "yolov3 an incremental improvement"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "sniper efficient multi scale training"
    - "soft sampling for robust object detection"
    - "exploiting web images for weakly supervised object detection"
    - "soft nms improving object detection with one line of code"
    - "zero annotation object detection with web knowledge transfer"
    - "extreme clicking for efficient object annotation"

Connected through hyperedge HE-5 (induced by source paper "boosted gan with semantically interpretable information for image inpainting"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "rethinking atrous convolution for semantic image segmentation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "hcp a flexible cnn framework for multi label image classification"
    - "high resolution image inpainting using multi scale neural patch synthesis"

Connected through hyperedge HE-6 (induced by source paper "multi label image recognition with graph convolutional networks"):
  with members:
    - "rpc a large scale retail product checkout dataset"
    - "chest x rays classification a multi label and fine grained problem"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "multi label zero shot learning with structured knowledge graphs"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"

Connected through hyperedge HE-7 (induced by source paper "multi moments in time learning and interpreting models for multi action video understanding"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "the something something video database for learning and evaluating visual common sense"
    - "temporal relational reasoning in videos"
    - "improving pairwise ranking for multi label image classification"
    - "hollywood in homes crowdsourcing data collection for activity understanding"

Connected through hyperedge HE-8 (induced by source paper "learning semantic specific graph representation for multi label image recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "multi label image classification via knowledge distillation from weakly supervised detection"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "learning spatial regularization with image level supervisions for multi label image classification"
    - "improving pairwise ranking for multi label image classification"

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)