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: "revisiting pre training an efficient training method for image classification"
  Abstract: The training method of repetitively feeding all samples into a pre-defined network for image classification has been widely adopted by current state-of-the-art. In this work, we provide a new method, which can be leveraged to train classification networks in a more efficient way. Starting with a warm-up step, we propose to continually repeat a Drop-and-Pick (DaP) learning strategy. In particular, we drop those easy samples to encourage the network to focus on studying hard ones. Meanwhile, by picking up all samples periodically during training, we aim to recall the memory of the networks to prevent catastrophic forgetting of previously learned knowledge. Our DaP learning method can recover 99.88%, 99.60%, 99.83% top-1 accuracy on ImageNet for ResNet-50, DenseNet-121, and MobileNet-V1 but only requires 75% computation in training compared to those using the classic training schedule. Furthermore, our pre-trained models are equipped with strong knowledge transferability when used for downstream tasks, especially for hard cases. Extensive experiments on object detection, instance segmentation and pose estimation can well demonstrate the effectiveness of our DaP training method.

Connected through hyperedge HE-1 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

Connected through hyperedge HE-2 (induced by source paper "data augmentation for object detection via progressive and selective instance switching"):
  with members:
    - "data augmentation using random image cropping and patching for deep cnns"
    - "dynamic curriculum learning for imbalanced data classification"
    - "class balanced loss based on effective number of samples"
    - "learning to segment via cut and paste"
    - "data augmentation by pairing samples for images classification"
    - "on the importance of visual context for data augmentation in scene understanding"
    - "compositional gan learning conditional image composition"
    - "modeling visual context is key to augmenting object detection datasets"

Connected through hyperedge HE-3 (induced by source paper "high frequency residual learning for multi scale image classification"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "deep high resolution representation learning for human pose estimation"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "slimmable neural networks"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "model compression via distillation and quantization"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "revisit multinomial logistic regression in deep learning data dependent model initialization for image recognition"

Connected through hyperedge HE-4 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "variational adversarial active learning"
    - "semi supervised learning with scarce annotations"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "deep clustering for unsupervised learning of visual features"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "unsupervised representation learning by predicting image rotations"

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)