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: "robust emotion recognition from low quality and low bit rate video a deep learning approach"
  Abstract: Emotion recognition from facial expressions is tremendously useful, especially when coupled with smart devices and wireless multimedia applications. However, the inadequate network bandwidth often limits the spatial resolution of the transmitted video, which will heavily degrade the recognition reliability. We develop a novel framework to achieve robust emotion recognition from low bit rate video. While video frames are downsampled at the encoder side, the decoder is embedded with a deep network model for joint super-resolution (SR) and recognition. Notably, we propose a novel max-mix training strategy, leading to a single "One-for-All" model that is remarkably robust to a vast range of downsampling factors. That makes our framework well adapted for the varied bandwidths in real transmission scenarios, without hampering scalability or efficiency. The proposed framework is evaluated on the AVEC 2016 benchmark, and demonstrates significantly improved stand-alone recognition performance, as well as rate-distortion (R-D) performance, than either directly recognizing from LR frames, or separating SR and recognition.

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"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "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"

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"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "dual dynamic inference enabling more efficient adaptive and controllable deep inference"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "slimmable neural networks"
    - "hybrid pruning thinner sparse networks for fast inference on edge devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"
    - "deep layer aggregation"

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"
    - "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 pre training an efficient training method for image classification"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving deep visual recognition an adversarial learning framework and a new dataset"):
  with members:
    - "towards privacy preserving visual recognition via adversarial training a pilot study"
    - "k beam minimax efficient optimization for deep adversarial learning"
    - "image to image translation for cross domain disentanglement"
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "adversarial image perturbation for privacy protection a game theory perspective"

Connected through hyperedge HE-6 (induced by source paper "towards privacy preserving visual recognition via adversarial training a pilot study"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "decouple learning for parameterized image operators"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "s3d single shot multi span detector via fully 3d convolutional networks"
    - "stereoscopic neural style transfer"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "density aware single image de raining using a multi stream dense network"
    - "deep exemplar based colorization"
    - "clearing the skies a deep network architecture for single image rain removal"
    - "global versus localized generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "improved techniques for learning to dehaze and beyond a collective study"):
  with members:
    - "enhance visual recognition under adverse conditions via deep networks"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "densely connected pyramid dehazing network"
    - "end to end united video dehazing and detection"
    - "focal loss for dense object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "benchmarking single image dehazing and beyond"
    - "an all in one network for dehazing and beyond"

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)