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: "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
  Abstract: Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises. In this paper, we propose Weakly Supervised Data Augmentation Network (WS-DAN) to explore the potential of data augmentation. Specifically, for each training image, we first generate attention maps to represent the object's discriminative parts by weakly supervised learning. Next, we augment the image guided by these attention maps, including attention cropping and attention dropping. The proposed WS-DAN improves the classification accuracy in two folds. In the first stage, images can be seen better since more discriminative parts' features will be extracted. In the second stage, attention regions provide accurate location of object, which ensures our model to look at the object closer and further improve the performance. Comprehensive experiments in common fine-grained visual classification datasets show that our WS-DAN surpasses the state-of-the-art methods, which demonstrates its effectiveness.

Connected through hyperedge HE-1 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "fine grained classification of cervical cells using morphological and appearance based convolutional neural networks"
    - "maximum entropy fine grained classification"
    - "adaptive neural trees"
    - "learn to pay attention"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised object localization with inter intra regulated cams"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "randaugment practical data augmentation with no separate search"
    - "fixing the train test resolution discrepancy"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "adversarial complementary learning for weakly supervised object localization"
    - "exploring the limits of weakly supervised pretraining"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-3 (induced by source paper "coarse2fine a two stage training method for fine grained visual classification"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "adversarial complementary learning for weakly supervised object localization"
    - "weakly supervised local attention network for fine grained visual classification"
    - "pairwise confusion for fine grained visual classification"
    - "residual attention network for image classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "count crop and recognise fine grained recognition in the wild"):
  with members:
    - "the via annotation software for images audio and video"
    - "face recognition primates in the wild"
    - "from benedict cumberbatch to sherlock holmes character identification in tv series without a script"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "c wsl count guided weakly supervised localization"
    - "the inaturalist species classification and detection dataset"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "logo 2k a large scale logo dataset for scalable logo classification"):
  with members:
    - "learning to navigate for fine grained classification"
    - "open logo detection challenge"
    - "scalable logo recognition using proxies"
    - "deep learning for logo recognition"
    - "open set logo detection and retrieval"
    - "automatic graphic logo detection via fast region based convolutional networks"
    - "deep learning logo detection with data expansion by synthesising context"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-6 (induced by source paper "learning to reinforcement learn for neural architecture search"):
  with members:
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "a study of the learning progress in neural architecture search techniques"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "blockqnn efficient block wise neural network architecture generation"
    - "efficient neural architecture search via parameter sharing"
    - "learning transferable architectures for scalable image recognition"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-7 (induced by source paper "fine grained classification of rowing teams"):
  with members:
    - "fixing the train test resolution discrepancy"
    - "attribute aware attention model for fine grained representation learning"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "attention is all you need"
    - "residual attention network for image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "why should i trust you explaining the predictions of any classifier"

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)