human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "face recognition in low quality images a survey"):
  Abstract: Low-resolution face recognition (LRFR) has received increasing attention over the past few years. Its applications lie widely in the real-world environment when high-resolution or high-quality images are hard to capture. One of the biggest demands for LRFR technologies is video surveillance. As the the number of surveillance cameras in the city increases, the videos that captured will need to be processed automatically. However, those videos or images are usually captured with large standoffs, arbitrary illumination condition, and diverse angles of view. Faces in these images are generally small in size. Several studies addressed this problem employed techniques like super resolution, deblurring, or learning a relationship between different resolution domains. In this paper, we provide a comprehensive review of approaches to low-resolution face recognition in the past five years. First, a general problem definition is given. Later, systematically analysis of the works on this topic is presented by catogory. In addition to describing the methods, we also focus on datasets and experiment settings. We further address the related works on unconstrained low-resolution face recognition and compare them with the result that use synthetic low-resolution data. Finally, we summarized the general limitations and speculate a priorities for the future effort.

Includes members:
  Member "on low resolution face recognition in the wild comparisons and new techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "coupled learning for facial deblur":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted schatten p norm minimization for image denoising and background subtraction")
  Member "super identity convolutional neural network for face hallucination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "deep cnn denoiser and multi layer neighbor component embedding for face hallucination":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep plug and play super resolution for arbitrary blur kernels")
  Member "low resolution face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "landmark detection in low resolution faces with semi supervised learning")
  Member "deep semantic face deblurring":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "human aware motion deblurring")
  Member "surveillance face recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "person re identification by contour sketch under moderate clothing change")
  Member "face super resolution through wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reshapegan object reshaping by providing a single reference image")

Question: Please tell me which class the source paper 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)