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: "rethinking deep active learning using unlabeled data at model training"
  Abstract: Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition. In this work we depart from this setting by using both labeled and unlabeled data during model training across active learning cycles. We do so by using unsupervised feature learning at the beginning of the active learning pipeline and semi-supervised learning at every active learning cycle, on all available data. The former has not been investigated before in active learning, while the study of latter in the context of deep learning is scarce and recent findings are not conclusive with respect to its benefit. Our idea is orthogonal to acquisition strategies by using more data, much like ensemble methods use more models. By systematically evaluating on a number of popular acquisition strategies and datasets, we find that the use of unlabeled data during model training brings a surprising accuracy improvement in image classification, compared to the differences between acquisition strategies. We thus explore smaller label budgets, even one label per class.

Connected through hyperedge HE-1 (induced by source paper "high accuracy and high fidelity extraction of neural networks"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "zero shot knowledge transfer via adversarial belief matching"
    - "a framework for the extraction of deep neural networks by leveraging public data"
    - "overlearning reveals sensitive attributes"
    - "combining mixmatch and active learning for better accuracy with fewer labels"
    - "on the learnability of deep random networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (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"
    - "variational adversarial active learning"
    - "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-3 (induced by source paper "adversarial representation active learning"):
  with members:
    - "bayesian generative active deep learning"
    - "large scale adversarial representation learning"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "variational adversarial active learning"
    - "high fidelity image generation with fewer labels"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gradient free policy architecture search and adaptation"
    - "generative adversarial active learning"

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)