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: "motion deblurring of faces"
  Abstract: Face analysis is a core part of computer vision, in which remarkable progress has been observed in the past decades. Current methods achieve recognition and tracking with invariance to fundamental modes of variation such as illumination, 3D pose, expressions. Notwithstanding, a much less standing mode of variation is motion deblurring, which however presents substantial challenges in face analysis. Recent approaches either make oversimplifying assumptions, e.g. in cases of joint optimization with other tasks, or fail to preserve the highly structured shape/identity information. Therefore, we propose a data-driven method that encourages identity preservation. The proposed model includes two parallel streams (sub-networks): the first deblurs the image, the second implicitly extracts and projects the identity of both the sharp and the blurred image in similar subspaces. We devise a method for creating realistic motion blur by averaging a variable number of frames to train our model. The averaged images originate from a 2MF2 dataset with 10 million facial frames, which we introduce for the task. Considering deblurring as an intermediate step, we utilize the deblurred outputs to conduct a thorough experimentation on high-level face analysis tasks, i.e. landmark localization and face verification. The experimental evaluation demonstrates the superiority of our method.

Connected through hyperedge HE-1 (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"
    - "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"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

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)