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: "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"
  Abstract: The conventional methods for estimating camera poses and scene structures from severely blurry or low resolution images often result in failure. The off-the-shelf deblurring or super-resolution methods may show visually pleasing results. However, applying each technique independently before matching is generally unprofitable because this naive series of procedures ignores the consistency between images. In this paper, we propose a pioneering unified framework that solves four problems simultaneously, namely, dense depth reconstruction, camera pose estimation, super-resolution, and deblurring. By reflecting a physical imaging process, we formulate a cost minimization problem and solve it using an alternating optimization technique. The experimental results on both synthetic and real videos show high-quality depth maps derived from severely degraded images that contrast the failures of naive multi-view stereo methods. Our proposed method also produces outstanding deblurred and super-resolved images unlike the independent application or combination of conventional video deblurring, super-resolution methods.

Connected through hyperedge HE-1 (induced by source paper "gated fusion network for degraded image super resolution"):
  with members:
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "scale recurrent network for deep image deblurring"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "self supervised linear motion deblurring"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "activestereonet end to end self supervised learning for active stereo systems"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-3 (induced by source paper "student becoming the master knowledge amalgamation for joint scene parsing depth estimation and more"):
  with members:
    - "amalgamating knowledge towards comprehensive classification"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "knowledge concentration learning 100k object classifiers in a single cnn"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "semi and weakly supervised semantic segmentation using generative adversarial network"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "davanet stereo deblurring with view aggregation"):
  with members:
    - "scale recurrent network for deep image deblurring"
    - "zoom and learn generalizing deep stereo matching to novel domains"
    - "stereoscopic neural style transfer"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "pyramid stereo matching network"
    - "online video deblurring via dynamic temporal blending network"
    - "motion deblurring in the wild"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "joint face super resolution and deblurring using a generative adversarial network"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "face de occlusion using 3d morphable model and generative adversarial network"
    - "arcface additive angular margin loss for deep face recognition"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-6 (induced by source paper "joint stereo video deblurring scene flow estimation and moving object segmentation"):
  with members:
    - "learning to extract a video sequence from a single motion blurred image"
    - "learning a discriminative prior for blind image deblurring"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "online video deblurring via dynamic temporal blending network"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "simultaneous stereo video deblurring and scene flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-7 (induced by source paper "fab a robust facial landmark detection framework for motion blurred videos"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "motion deblurring of faces"
    - "style aggregated network for facial landmark detection"
    - "deep semantic face deblurring"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "online video deblurring via dynamic temporal blending network"
    - "deep face deblurring"
    - "learning blind motion deblurring"

Connected through hyperedge HE-8 (induced by source paper "adversarial spatio temporal learning for video deblurring"):
  with members:
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "stereo video deblurring"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"
    - "deep video deblurring"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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)