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: "variational adversarial active learning"
  Abstract: Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning algorithms, our approach is task agnostic, i.e., it does not depend on the performance of the task for which we are trying to acquire labeled data. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to discriminate between unlabeled and labeled data. The mini-max game between the VAE and the adversarial network is played such that while the VAE tries to trick the adversarial network into predicting that all data points are from the labeled pool, the adversarial network learns how to discriminate between dissimilarities in the latent space. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and establish a new state of the art on $\text{CIFAR10/100}$, $\text{Caltech-256}$, $\text{ImageNet}$, $\text{Cityscapes}$, and $\text{BDD100K}$. Our results demonstrate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and provides for a computationally efficient sampling method. Our code is available at this https URL.

Connected through hyperedge HE-1 (induced by source paper "task aware variational adversarial active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "learning loss for active learning"
    - "ranking cgans subjective control over semantic image attributes"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "deep bayesian active learning with image data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "dibs diversity inducing information bottleneck in model ensembles"):
  with members:
    - "mim mutual information machine"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "learning not to learn training deep neural networks with biased data"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "attention based ensemble for deep metric learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "dilated residual networks"

Connected through hyperedge HE-3 (induced by source paper "deep active learning for biased datasets via fisher kernel self supervision"):
  with members:
    - "remap multi layer entropy guided pooling of dense cnn features for image retrieval"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "interpreting black box predictions using fisher kernels"
    - "unsupervised representation learning by predicting image rotations"
    - "learning to reweight examples for robust deep learning"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-4 (induced by source paper "mining gold samples for conditional gans"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"
    - "cgans with projection discriminator"
    - "the relativistic discriminator a key element missing from standard gan"
    - "spectral normalization for generative adversarial networks"
    - "not all samples are created equal deep learning with importance sampling"
    - "discriminative active learning"

Connected through hyperedge HE-5 (induced by source paper "training data distribution search with ensemble active learning"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "semantic redundancies in image classification datasets the 10 you don t need"
    - "automl a survey of the state of the art"
    - "are all training examples created equal an empirical study"
    - "how transferable are the datasets collected by active learners"
    - "amc automl for model compression and acceleration on mobile devices"
    - "adaptive semantic segmentation with a strategic curriculum of proxy labels"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-6 (induced by source paper "parting with illusions about deep active learning"):
  with members:
    - "semi supervised semantic segmentation needs strong high dimensional perturbations"
    - "semi supervised semantic segmentation with high and low level consistency"
    - "rethinking deep active learning using unlabeled data at model training"
    - "consistency based semi supervised active learning towards minimizing labeling budget"
    - "unsupervised data augmentation for consistency training"
    - "learning loss for active learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "consistency regularization and cutmix for semi supervised semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "rethinking deep active learning using unlabeled data at model training"):
  with members:
    - "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"
    - "revisiting pre training an efficient training method for image classification"
    - "large scale visual active learning with deep probabilistic ensembles"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-8 (induced by source paper "deep active learning unified and principled method for query and training"):
  with members:
    - "bayesian generative active deep learning"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "a principled approach for learning task similarity in multitask learning"
    - "adversarial active learning for deep networks a margin based approach"
    - "adversarial sampling for active learning"
    - "generative adversarial active learning"
    - "learning algorithms for active learning"
    - "improved training of wasserstein gans"

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)