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: "adversarial spatio temporal learning for video deblurring"
  Abstract: Camera shake or target movement often leads to undesired blur effects in videos captured by a hand-held camera. Despite significant efforts having been devoted to video-deblur research, two major challenges remain: 1) how to model the spatio-temporal characteristics across both the spatial domain (i.e., image plane) and the temporal domain (i.e., neighboring frames) and 2) how to restore sharp image details with respect to the conventionally adopted metric of pixel-wise errors. In this paper, to address the first challenge, we propose a deblurring network (DBLRNet) for spatial-temporal learning by applying a 3D convolution to both the spatial and temporal domains. Our DBLRNet is able to capture jointly spatial and temporal information encoded in neighboring frames, which directly contributes to the improved video deblur performance. To tackle the second challenge, we leverage the developed DBLRNet as a generator in the generative adversarial network (GAN) architecture and employ a content loss in addition to an adversarial loss for efficient adversarial training. The developed network, which we name as deblurring GAN, is tested on two standard benchmarks and achieves the state-of-the-art performance.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "deep visual attention prediction"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "how drones look crowdsourced knowledge transfer for aerial video saliency prediction"
    - "deep mutual learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "using deep networks for drone detection"

Connected through hyperedge HE-2 (induced by source paper "blurry video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "feedback network for image super resolution"
    - "refine and distill exploiting cycle inconsistency and knowledge distillation for unsupervised monocular depth estimation"
    - "temporal cycle consistency learning"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "video enhancement with task oriented flow"
    - "video to video synthesis"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal video re localization by warp lstm"):
  with members:
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "a better baseline for ava"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "actor centric relation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"

Connected through hyperedge HE-4 (induced by source paper "deep video deblurring the devil is in the details"):
  with members:
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-5 (induced by source paper "edvr video restoration with enhanced deformable convolutional networks"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "path restore learning network path selection for image restoration"
    - "video enhancement with task oriented flow"
    - "frame recurrent video super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "object detection in video with spatiotemporal sampling networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "scale recurrent network for deep image deblurring"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal adversarial learning for detecting unseen falls"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "satellite image forgery detection and localization using gan and one class classifier"
    - "adversarially learned one class classifier for novelty detection"
    - "deepfall non invasive fall detection with deep spatio temporal convolutional autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-7 (induced by source paper "temporally coherent video harmonization using adversarial networks"):
  with members:
    - "mhp vos multiple hypotheses propagation for video object segmentation"
    - "imagine this scripts to compositions to videos"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "dual motion gan for future flow embedded video prediction"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"

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)