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: "attention based dropout layer for weakly supervised object localization"
  Abstract: Weakly Supervised Object Localization (WSOL) techniques learn the object location only using image-level labels, without location annotations. A common limitation for these techniques is that they cover only the most discriminative part of the object, not the entire object. To address this problem, we propose an Attention-based Dropout Layer (ADL), which utilizes the self-attention mechanism to process the feature maps of the model. The proposed method is composed of two key components: 1) hiding the most discriminative part from the model for capturing the integral extent of object, and 2) highlighting the informative region for improving the recognition power of the model. Based on extensive experiments, we demonstrate that the proposed method is effective to improve the accuracy of WSOL, achieving a new state-of-the-art localization accuracy in CUB-200-2011 dataset. We also show that the proposed method is much more efficient in terms of both parameter and computation overheads than existing techniques.

Connected through hyperedge HE-1 (induced by source paper "dual attention guided dropblock module for weakly supervised object localization"):
  with members:
    - "survey of dropout methods for deep neural networks"
    - "self produced guidance for weakly supervised object localization"
    - "dropblock a regularization method for convolutional networks"
    - "adversarial complementary learning for weakly supervised object localization"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "tell me where to look guided attention inference network"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-2 (induced by source paper "rethinking the route towards weakly supervised object localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "objects as points"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "joint optimization framework for learning with noisy labels"
    - "adversarial complementary learning for weakly supervised object localization"
    - "dual path networks"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-3 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "robust attribution regularization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "harnessing the vulnerability of latent layers in adversarially trained models"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-4 (induced by source paper "relation graph network for 3d object detection in point clouds"):
  with members:
    - "learning object bounding boxes for 3d instance segmentation on point clouds"
    - "std sparse to dense 3d object detector for point cloud"
    - "pixor real time 3d object detection from point clouds"
    - "centernet keypoint triplets for object detection"
    - "fan focused attention networks"
    - "adaptively connected neural networks"
    - "learning actor relation graphs for group activity recognition"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-5 (induced by source paper "cutmix regularization strategy to train strong classifiers with localizable features"):
  with members:
    - "dropblock a regularization method for convolutional networks"
    - "improved mixed example data augmentation"
    - "modeling visual context is key to augmenting object detection datasets"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial complementary learning for weakly supervised object localization"
    - "shakedrop regularization for deep residual learning"
    - "exploring the limits of weakly supervised pretraining"
    - "mixup as locally linear out of manifold regularization"

Connected through hyperedge HE-6 (induced by source paper "on the detection of digital face manipulation"):
  with members:
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "advfaces adversarial face synthesis"
    - "deep tree learning for zero shot face anti spoofing"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"

Connected through hyperedge HE-7 (induced by source paper "looking at the right stuff guided semantic gaze for autonomous driving"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "dynamic traffic scene classification with space time coherence"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "salient object detection a survey"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"

Connected through hyperedge HE-8 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "object part attention model for fine grained image classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "deep descriptor transforming for image co 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)