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: "soft proposal networks for weakly supervised object localization"
  Abstract: Weakly supervised object localization remains challenging, where only image labels instead of bounding boxes are available during training. Object proposal is an effective component in localization, but often computationally expensive and incapable of joint optimization with some of the remaining modules. In this paper, to the best of our knowledge, we for the first time integrate weakly supervised object proposal into convolutional neural networks (CNNs) in an end-to-end learning manner. We design a network component, Soft Proposal (SP), to be plugged into any standard convolutional architecture to introduce the nearly cost-free object proposal, orders of magnitude faster than state-of-the-art methods. In the SP-augmented CNNs, referred to as Soft Proposal Networks (SPNs), iteratively evolved object proposals are generated based on the deep feature maps then projected back, and further jointly optimized with network parameters, with image-level supervision only. Through the unified learning process, SPNs learn better object-centric filters, discover more discriminative visual evidence, and suppress background interference, significantly boosting both weakly supervised object localization and classification performance. We report the best results on popular benchmarks, including PASCAL VOC, MS COCO, and ImageNet.

Connected through hyperedge HE-1 (induced by source paper "few shot few shot learning and the role of spatial attention"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "large scale long tailed recognition in an open world"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "deep clustering for unsupervised learning of visual features"

Connected through hyperedge HE-2 (induced by source paper "in defense of graph inference algorithms for weakly supervised object localization"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "learning to find common objects across few image collections"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "note rcnn noise tolerant ensemble rcnn for semi supervised object detection"
    - "dissimilarity coefficient based weakly supervised object detection"
    - "sniper efficient multi scale training"
    - "focal loss for dense object detection"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-3 (induced by source paper "few example object detection with model communication"):
  with members:
    - "zero shot detection"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "active self paced learning for cost effective and progressive face identification"
    - "collaborative learning for weakly supervised object detection"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "learning to segment human by watching youtube"

Connected through hyperedge HE-4 (induced by source paper "attention based dropout layer for weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "few example object detection with model communication"
    - "bam bottleneck attention module"
    - "image transformer"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-5 (induced by source paper "instance segmentation with point supervision"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "object counting and instance segmentation with image level supervision"
    - "weakly supervised instance segmentation using class peak response"
    - "learning to segment via cut and paste"
    - "where are the blobs counting by localization with point supervision"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "on the importance of visual context for data augmentation in scene understanding"

Connected through hyperedge HE-6 (induced by source paper "activity driven weakly supervised object detection"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "weakly supervised video object grounding from text by loss weighting and object interaction"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "joint discovery of object states and manipulation actions"
    - "detecting and recognizing human object interactions"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-7 (induced by source paper "marginalized average attentional network for weakly supervised learning"):
  with members:
    - "adversarial complementary learning for weakly supervised object localization"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "structured attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "temporal action detection with structured segment networks"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "untrimmednets for weakly supervised action recognition and detection"

Connected through hyperedge HE-8 (induced by source paper "min entropy latent model for weakly supervised object detection"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "from bow to cnn two decades of texture representation for texture classification"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "deep learning for generic object detection a survey"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "label refinery improving imagenet classification through label progression"
    - "deep self taught learning for weakly supervised object localization"
    - "two phase learning for weakly supervised object localization"

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)