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: "c wsl count guided weakly supervised localization"
  Abstract: We introduce a count-guided weakly supervised localization (C-WSL) framework with per-class object count as an additional form of image-level supervision to improve weakly supervised localization (WSL). C-WSL uses a simple count-based region selection algorithm to select high quality regions, each of which covers a single object instance at training time, and improves WSL by training with the selected regions. To demonstrate the effectiveness of C-WSL, we integrate object count supervision into two WSL architectures and conduct extensive experiments on Pascal VOC2007 and VOC2012. Experimental results show that C-WSL leads to large improvements in WSL detection performance and that the proposed approach significantly outperforms the state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "object instance mining for weakly supervised object detection"):
  with members:
    - "weakly supervised object detection with segmentation collaboration"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "dissimilarity coefficient based weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"
    - "multiple instance detection network with online instance classifier refinement"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "wslln weakly supervised natural language localization networks"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "weakly supervised dense event captioning in videos"
    - "temporal recurrent networks for online action detection"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "to find where you talk temporal sentence localization in video with attention based location regression"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "temporal action detection with structured segment networks"

Connected through hyperedge HE-5 (induced by source paper "towards precise end to end weakly supervised object detection network"):
  with members:
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "attention is all you need"
    - "deep self taught learning for weakly supervised object localization"
    - "squeeze and excitation networks"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-6 (induced by source paper "towards partial supervision for generic object counting in natural scenes"):
  with members:
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "learning spatial awareness to improve crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "where are the masks instance segmentation with image level supervision"
    - "object counting and instance segmentation with image level supervision"
    - "from open set to closed set counting objects by spatial divide and conquer"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "where are the blobs counting by localization with point supervision"

Connected through hyperedge HE-7 (induced by source paper "wsod 2 learning bottom up and top down objectness distillation for weakly supervised object detection"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "zigzag learning for weakly supervised object detection"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "collaborative learning for weakly supervised object detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "pcl proposal cluster learning for weakly supervised object detection"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-8 (induced by source paper "dual attention focused module for weakly supervised object localization"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "self produced guidance for weakly supervised object localization"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "adversarial complementary learning for weakly supervised object localization"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"

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)