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: "enhance visual recognition under adverse conditions via deep networks"
  Abstract: Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage. While deep neural networks have been extensively exploited in the techniques of low-quality image restoration and high-quality image recognition tasks, respectively, few studies have been done on the important problem of recognition from very low-quality images. This paper proposes a deep learning-based framework for improving the performance of image and video recognition models under adverse conditions, using robust adverse pre-training or its aggressive variant. The robust adverse pre-training algorithms leverage the power of pre-training and generalize the conventional unsupervised pre-training and data augmentation methods. We further develop a transfer learning approach to cope with real-world datasets of unknown adverse conditions. The proposed framework is comprehensively evaluated on a number of image and video recognition benchmarks, and obtains significant performance improvements under various single or mixed adverse conditions. Our visualization and analysis further add to the explainability of the results.

Connected through hyperedge HE-1 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "enlightengan deep light enhancement without paired supervision"
    - "deep plug and play super resolution for arbitrary blur kernels"
    - "joint enhancement and denoising method via sequential decomposition"
    - "getting to know low light images with the exclusively dark dataset"
    - "yolov3 an incremental improvement"
    - "pyramidbox a context assisted single shot face detector"
    - "real world noisy image denoising a new benchmark"

Connected through hyperedge HE-2 (induced by source paper "segmentation aware image denoising without knowing true segmentation"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "bridging the gap between computational photography and visual recognition"
    - "yolov3 an incremental improvement"
    - "toward convolutional blind denoising of real photographs"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "deep semantic face deblurring"

Connected through hyperedge HE-3 (induced by source paper "delving into robust object detection from unmanned aerial vehicles a deep nuisance disentanglement approach"):
  with members:
    - "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"
    - "bridging the gap between computational photography and visual recognition"
    - "vision meets drones a challenge"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "yolov3 an incremental improvement"
    - "modeling visual context is key to augmenting object detection datasets"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "dada deep adversarial data augmentation for extremely low data regime classification"

Connected through hyperedge HE-4 (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"
    - "slimmable neural networks"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "model compression via distillation and quantization"
    - "revisiting pre training an efficient training method for image classification"
    - "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-5 (induced by source paper "low resolution face recognition in the wild via selective knowledge distillation"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "low resolution face recognition in the wild"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "synthesis based robust low resolution face recognition"
    - "unconstrained face detection and open set face recognition challenge"
    - "data distillation towards omni supervised learning"
    - "darkrank accelerating deep metric learning via cross sample similarities transfer"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-6 (induced by source paper "all in one underwater image enhancement using domain adversarial learning"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "bridging the gap between computational photography and visual recognition"
    - "an underwater image enhancement benchmark dataset and beyond"
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "deep underwater image enhancement"
    - "yolov3 an incremental improvement"
    - "enhancing underwater imagery using generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "u finger multi scale dilated convolutional network for fingerprint image denoising and inpainting"):
  with members:
    - "improved techniques for learning to dehaze and beyond a collective study"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "studying very low resolution recognition using deep networks"
    - "multi scale context aggregation by dilated convolutions"
    - "learning super resolution jointly from external and internal examples"

Connected through hyperedge HE-8 (induced by source paper "towards privacy preserving visual recognition via adversarial training a pilot study"):
  with members:
    - "extreme low resolution activity recognition with multi siamese embedding learning"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "learning transferable architectures for scalable image recognition"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "privacy preserving visual learning using doubly permuted homomorphic encryption"
    - "adversarial image perturbation for privacy protection a game theory perspective"
    - "robust emotion recognition from low quality and low bit rate video a deep learning approach"

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)