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: "adversarial complementary learning for weakly supervised object localization"
  Abstract: In this work, we propose Adversarial Complementary Learning (ACoL) to automatically localize integral objects of semantic interest with weak supervision. We first mathematically prove that class localization maps can be obtained by directly selecting the class-specific feature maps of the last convolutional layer, which paves a simple way to identify object regions. We then present a simple network architecture including two parallel-classifiers for object localization. Specifically, we leverage one classification branch to dynamically localize some discriminative object regions during the forward pass. Although it is usually responsive to sparse parts of the target objects, this classifier can drive the counterpart classifier to discover new and complementary object regions by erasing its discovered regions from the feature maps. With such an adversarial learning, the two parallel-classifiers are forced to leverage complementary object regions for classification and can finally generate integral object localization together. The merits of ACoL are mainly two-fold: 1) it can be trained in an end-to-end manner; 2) dynamically erasing enables the counterpart classifier to discover complementary object regions more effectively. We demonstrate the superiority of our ACoL approach in a variety of experiments. In particular, the Top-1 localization error rate on the ILSVRC dataset is 45.14%, which is the new state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "explainable deep classification models for domain generalization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "addressing model vulnerability to distributional shifts over image transformation sets"
    - "episodic training for domain generalization"
    - "domain generalization by solving jigsaw puzzles"
    - "weakly supervised instance segmentation using class peak response"
    - "excitation dropout encouraging plasticity in deep neural networks"
    - "moment matching for multi source domain adaptation"
    - "generalizing across domains via cross gradient training"

Connected through hyperedge HE-2 (induced by source paper "dual attention guided dropblock module for weakly supervised object localization"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "survey of dropout methods for deep neural networks"
    - "self produced guidance for weakly supervised object localization"
    - "dropblock a regularization method for convolutional networks"
    - "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-3 (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"
    - "attention based dropout layer for weakly supervised object 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"
    - "dual path networks"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"

Connected through hyperedge HE-4 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based dropout layer for weakly supervised object 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"

Connected through hyperedge HE-5 (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"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cbam convolutional block attention module"
    - "tell me where to look guided attention inference network"

Connected through hyperedge HE-6 (induced by source paper "cutmix regularization strategy to train strong classifiers with localizable features"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "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"
    - "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-7 (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"
    - "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"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-8 (induced by source paper "bae net branched autoencoder for shape co segmentation"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deep part induction from articulated object pairs"
    - "3d point capsule networks"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "learning implicit fields for generative shape modeling"

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)