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: "generative adversarial active learning"
  Abstract: We propose a new active learning by query synthesis approach using Generative Adversarial Networks (GAN). Different from regular active learning, the resulting algorithm adaptively synthesizes training instances for querying to increase learning speed. We generate queries according to the uncertainty principle, but our idea can work with other active learning principles. We report results from various numerical experiments to demonstrate the effectiveness the proposed approach. In some settings, the proposed algorithm outperforms traditional pool-based approaches. To the best our knowledge, this is the first active learning work using GAN.

Connected through hyperedge HE-1 (induced by source paper "baod budget aware object detection"):
  with members:
    - "a flexible model for training action localization with varying levels of supervision"
    - "pcn part and context information for pedestrian detection with cnns"
    - "crowd counting by adaptively fusing predictions from an image pyramid"
    - "localization aware active learning for object detection"
    - "towards practical visual search engine within elasticsearch"
    - "learning active learning from data"
    - "extreme clicking for efficient object annotation"
    - "deep self taught learning for weakly supervised object localization"

Connected through hyperedge HE-2 (induced by source paper "active adversarial domain adaptation"):
  with members:
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "open set domain adaptation by backpropagation"
    - "cross domain weakly supervised object detection through progressive domain adaptation"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "partial adversarial domain adaptation"
    - "localization aware active learning for object detection"

Connected through hyperedge HE-3 (induced by source paper "thinking outside the pool active training image creation for relative attributes"):
  with members:
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "active decision boundary annotation with deep generative models"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"
    - "shape from shading through shape evolution"

Connected through hyperedge HE-4 (induced by source paper "variational adversarial active learning"):
  with members:
    - "uncertainty guided continual learning with bayesian neural networks"
    - "generalized zero and few shot learning via aligned variational autoencoders"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cost sensitive active learning for intracranial hemorrhage detection"
    - "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"
    - "adversarial sampling for active learning"
    - "gradient free policy architecture search and adaptation"
    - "cost effective active learning for melanoma segmentation"

Connected through hyperedge HE-5 (induced by source paper "active image synthesis for efficient labeling"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "generative invertible networks gin pathophysiology interpretable feature mapping and virtual patient generation"
    - "towards deeper understanding of variational autoencoding models"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"

Connected through hyperedge HE-6 (induced by source paper "the importance of metric learning for robotic vision open set recognition and active learning"):
  with members:
    - "deep metric learning and image classification with nearest neighbour gaussian kernels"
    - "metric learning for novelty and anomaly detection"
    - "novelty detection with gan"
    - "cost effective active learning for deep image classification"
    - "learning how to active learn a deep reinforcement learning approach"
    - "distance based confidence score for neural network classifiers"
    - "smart mining for deep metric learning"
    - "deep metric learning via facility location"

Connected through hyperedge HE-7 (induced by source paper "bayesian generative active deep learning"):
  with members:
    - "adversarial active learning for deep networks a margin based approach"
    - "discriminative active learning"
    - "scalable and accurate deep learning for electronic health records"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"
    - "a bayesian data augmentation approach for learning deep models"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-8 (induced by source paper "disentanglement based active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "generative adversarial network in medical imaging a review"
    - "how good is my gan"
    - "label noise robust generative adversarial networks"
    - "structured generative adversarial networks"
    - "triple generative adversarial nets"
    - "guiding infogan with semi supervision"

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.LG(Machine Learning)